Nicolas Iafrate

Mickael Matrat
email: mickael.matrat@ifpen.fr

Jean-Marc Zaccardi

Numerical investigations on hydrogen-enhanced combustion in ultra-lean gasoline spark-ignition engines

Keywords: Lean combustion, RANS simulation, hydrogen, combustion enhancer, spark engine combustion

Performance of lean burn gasoline spark-ignition engines can be enhanced through hydrogen supplementation. Thanks to its physicochemical properties, hydrogen supports the flame propagation and extends the dilution limits with improved combustion stability. These interesting features usually result in decreased emissions and improved efficiencies. This article aims at demonstrating how hydrogen can support the combustion process with a modern combustion system optimized for high dilution resistance and efficiency. To achieve this, chemical kinetics calculations are first performed in order to quantify the impacts of hydrogen addition on the laminar flame speed and on the auto-ignition delay times of air/gasoline mixtures. These data are then implemented in the ECFM and TKI combustion models in a specifically updated version of the CONVERGE code. 3D CFD engine calculations are performed at λ = 2 with 3 v/v.% of hydrogen for two operating points. At low load, numerical investigations show that hydrogen enhances the maximal combustion speed and the flame growth just after the spark which is a critical aspect of combustion with diluted mixtures. The flame front propagation is also more isotropic when supported with hydrogen. At mid load, hydrogen improves the combustion speed but also extends the auto-ignition delay times resulting in a better knocking resistance. A maximal indicated efficiency of 48.5% can thus be reached at λ = 2 thanks to an optimal combustion timing.

Introduction

To reduce fuel consumption and pollutant emissions of Spark-Ignition (SI) engines, car manufacturers have increasingly focused on alternative fuels and lean burn combustion. Enhanced aerodynamic and ignition characteristics are two key parameters that impact the lean combustion stability (1). Besides these technical aspects, fuel formulation can also contribute to perform stable lean combustion. Hydrogen has proved to be a good alternative fuel for vehicles when used as the single source of chemical energy or as a supplemental combustion enhancer for Diesel or SI engines (2; 3). The unique combustion properties of hydrogen can lead to significant benefits in terms of engine efficiency and emissions performance. For example the adiabatic flame speed is five times larger for hydrogen (237 cm/s) than for gasoline (42 cm/s) which contributes to extend the engine operating range with an acceptable combustion stability. Furthermore, the diffusion coefficient of hydrogen is around four times as large as that of gasoline (0.61 and 0.16 cm 2 /s respectively), which improves the hydrogen-air mixing process. Despite all these advantages, the pure hydrogen-fueled engine could not be widely commercialized because of several reasons and notably because of its poor NOx emissions performance (4) related to its high adiabatic flame temperature. Moreover, the energy density of hydrogen is much lower than that of gasoline, leading to a weak torque output [START_REF] Ganesh | Hydrogen fueled spark ignition engine with electronically controlled manifold injection: An experimental study[END_REF]. However, using a small amount of hydrogen as an additive to hydrocarbonfueled engines is a good compromise to take advantage of the good properties of both fuels. Such a strategy is already well known in the literature and several good results have already been reported (6; 7; 8; 9). It is particularly the case for lean burn SI engines for which hydrogen supplementation was considered since the 1970s [START_REF] Houseman | A Two-Charge Engine Concept: Hydrogen Enrichment[END_REF] in order to improve the dilution resistance, to improve engine thermal efficiency and to reduce pollutant emissions (11; 12). Ji el al. (13; 14; 15) conducted a series of experimental investigations on the hybrid hydrogen-gasoline engine. The results showed that hydrogen blending availed heightening the engine thermal efficiency, reducing pollutants and reducing the cycle to cycle variation. Several experimental studies have been performed to better understand the performance of hydrogenenriched or hydrogen-supplemented engines. For example, Varde et al. [START_REF] Varde | Combustion Characteristics of Small Spark Ignition Engines Using Hydrogen Supplemented Fuel Mixtures[END_REF] have investigated the effect of hydrogen addition on the engine lean operating stability with a singlecylinder carburetor engine. They conclude that hydrogen addition lead to an increase of the flame development and propagation. The hydrogen addition under lean and diluted conditions was investigated by Tahtouh et al. on a single cylinder engine [START_REF] Tahtouh | Effects of hydrogen addition under lean and diluted conditions on combustion characteristics and emissions in a sparkignition engine[END_REF]. They demonstrated that hydrogen addition combined with lean and diluted operation can impact positively HC, CO and nitrogen oxides emissions. The effect of various equivalence ratios and speeds on combustion of a gasoline engine with hydrogen addition was also studied by D'Andrea et al. [START_REF] Dandrea | The addition of hydrogen to a gasolinefuelled SI engine[END_REF], on a modified carburetor engine with a premixed air/hydrogen mixture at the intake, which highlight that the hydrogen increase the combustion speed.

In the future, ultra-lean mixtures in spark-ignition engines will be required to achieve very high efficiency and hydrogen supplementation is a potential solution to support the combustion process in those ultra-lean conditions targeting λ ≥ 2, and maximal brake thermal efficiencies higher than 50%. Other combustion systems relying on pure hydrogen combustion could also be considered as suggested by Gerke et al. [START_REF] Gerke | Three-dimensional computational fluid dynamics simulation of hydrogen engines using a turbulent flame speed closure combustion model[END_REF] who focus on key parameters such as the flame speed to model this system.

The aim of this study is to compare the results of some experimental investigations with 3D CFD calculations in order to highlight the effect of hydrogen on the combustion process for different operating points. A threesteps methodology has been defined for this work. In the first step, chemical kinetics calculations were performed in homogeneous reactors to determine the impact of hydrogen on the laminar flame velocity and on the auto-ignition delay times of air/gasoline mixtures. In a second step, the combustion models and the CONVERGE calculation code were updated to incorporate the information provided by detailed chemical kinetics calculations. Validation calculations were then performed with CONVERGE on simple reference cases. Finally, 3D engine calculations were carried out for two operating points in order to highlight the positive effects of hydrogen on combustion in SI engines. The following sections detail first the chemical kinetics calculations and the engine characteristics. Then, the numerical setup for engine calculations is detailed as well as the numerical results obtained for two operating points.

Kinetic modeling of the H 2 boosted combustion

Chemical kinetic models enable to explore the detailed reactivity of selected compounds within certain ranges of thermodynamic conditions or mixtures compositions. Estimation of global combustion properties such as autoignition delays or laminar flame speeds is possible but the model ability to extrapolate these parameters outside of their validation ranges can be uncertain and mostly depends on the way they were established. In order to develop the inputs required for the 3D calculations performed in the frame of this study with hydrogen, two distincts mechanisms were used based on their ability to perform well for the existing experimental data regarding auto-ignition delays or laminar flame speeds. Due to limited data for the developed surrogate fuel in combination with hydrogen, it must be emphasized that the chemical inputs developed here mainly aim to capture the qualitative trends when an extrapolation is required. Various tests were performed and presented here to ensure the representativity of the chemical inputs provided to the CONVERGE simulations.

Surrogate fuel establishment

Each mechanism selected to simulate the auto-ignition delays or the laminar flame speeds requires a satisfactory estimation of the gasoline surrogate compounds and hydrogen properties. Common compounds used to model gasoline combustion comprise mainly a mix of three species namely n-heptane, iso-octane and toluene with the addition of ethanol to account for the use of oxygenated fuels as blending components [START_REF] Sarathy | Recent progress in gasoline surrogate fuels[END_REF]. In order to propose a surrogate composition that is representative of the gasoline fuel simulated, the reaction workbench from CHEMKIN Pro was used [START_REF] Chemkin-Pro | Reaction Design[END_REF]. It includes a fuel surrogate formulation that enables to match both the physical and chemical properties of the real fuel. The four compounds cited above were used and key properties including the lower heating value, octane numbers, ratios of each chemical family (e.g. alkane, aromatic, alcohol) as well as the elemental composition were targetted. The surrogate defined, here named TRFE for Toluene Reference Fuel containing Ethanol, includes 30.8 % of toluene, 42.4 % of iso-octane, 17.6 % of n-heptane and 9.2 % of ethanol in liquid volume. This matches within 3 v/v.% (liquid volume) the E10 gasoline fuel composition for each chemical family. In addition, the octane numbers estimated based on individual components were 94.7 and 87.8 for the research octane number and the motor octane number respectively. This gasoline fuel surrogate was used in combination of two kinetic mechanisms chosen for autoignition and laminar flame speed modeling.

Laminar flame speed modeling

The first mechanism dedicated to laminar flame speeds calculations is based on the reduced kinetic model developed for gasoline surrogate as well as natural gas by Bounaceur et al. [START_REF] Bounaceur | Modeling the Laminar Flame Speed of Natural Gas and Gasoline Surrogates[END_REF] to which a skeletal sub-mechanism for toluene oxidation has been implemented. The mechanism used includes 2118 reactions and 276 species. Its validation is established within common pressure, temperature and equivalence ratios ranges considering the current thermodynamic experimental limitations of dedicated apparatus. Indeed, the temperature range considered is usually 298 K -500 K for heavier hydrocarbons than C 3 and a maximum pressure lower than 25 atm (23; 20). These conditions are consequently at the edge of the targetted cases in this work. Qualitative trends are thus the objective for each parameter variation considered. Here the laminar flame speed is numerically simulated using the PREMIX and AURORA libraries of CHEMKIN-PRO package version 15131 [START_REF] Chemkin-Pro | Reaction Design[END_REF]. For all calculations results presented here and used for the flame speed correlation development, simulations converged for gradient and curvature criteria equal to 0.5. Mixture-averaged transport was used in order to screen a larger set of thermodynamic and mixtures conditions for the flame speed correlation establishment. It was found that this methodology provides satisfactory estimations of the flame speeds trends versus the several parameters considered.

As presented previously [START_REF] Bounaceur | Modeling the Laminar Flame Speed of Natural Gas and Gasoline Surrogates[END_REF], the mechanism proposed enables to simulate with a good agreement flame speeds of single hydrocarbons and mixtures thereof up to C 7 and C 8 . In addition, its ability to model pure hydrogen combustion is illustrated Figures 1a, 1b and2. The order of magnitude and the trends are well simulated for the pressure and λ variations. The pressure effect illustrated Figure 1 is correctly captured by the model. Indeed, the data set from Iijima et al. [START_REF] Iijima | Effects of temperature and pressure on burning velocity[END_REF] overestimates the laminar flame speed at high pressure as the flame stretch is not considered in their study. This finding has been confirmed by Aung et al. (27) who measured a laminar flame speed close to 2 m/s at 4 bar. The temperature effect illustrated in Figure 2 is correctly captured for lean mixtures while a consistent deviation is observed as the temperature increases or λ decreases. The simulations refinement used is identical to the parameters selected for the flame speed database establishement. This clearly affects the results and can be the reason for this discrepancy as a higher refinement (i.e. gradient and curvature down to 0.05) modifies the laminar flame speeds values up to 15% for a large set of tested cases.

Considering the hydrogen effect on fuel surrogate flame speed, the doping level considered here does not exceed 6 v/v.% of the intake air flow rate. A volumetric value of hydrogen within the fuel composition higher than 70% can then be reached in this study. A few studies highlight an increase of the laminar flame speed for iso-octane when hydrogen is added independantly of the equivalence ratio (29; 30; 31). As an example, the laminar flame speed increase at 423 K and λ = 1.25 is close to 10% and 20% for a volumetric hydrogen addition to iso-octane of 50% and 70% respectively [START_REF] Bouvet | On the effective Lewis number formulations for lean hydrogen/hydrocarbon/air mixtures[END_REF]. Figure 3 compares the laminar flame speed boost effect of hydrogen for the fuel surrogate considered here for similar thermodynamic conditions than the later study with iso-octane.

While the fuels are different, the predicted boost is similar. The result suggests that hydrogen addition to hydrocarbons leads to a similar effect. This statement is supported by Bouvet et al. [START_REF] Bouvet | On the effective Lewis number formulations for lean hydrogen/hydrocarbon/air mixtures[END_REF] for smaller hydrocarbons such as propane or methane even if the later displays a higher boost relative to pure methane flame speed for lower amounts of hydrogen added.

In order to evaluate and store the hydrogen boost on a large set of parameters for the 3D calculations, the kinetic mechanism was then used to develop a flame speed correlation. A matlab algorithm including a nonlinear optimization function was used to obtain the equation coefficients. The mathematical equation form as well as the coefficients are provided in Table 1.

S L =S L0 T T 0 h P P 0 i (1 + jXH 2 (Φ -1)) 1 + k.EGR 2 + l.EGR (1 -m.EGR.XH 2 ) (1)
Where

S L0 = (1 + a(XH 2 ) b )cΦ d exp(e(|Φ + f |) 2 ) (2)
Constants are reported in Table 1. The flame speed is reported in cm/s and S L0 corresponds to the laminar flame speed in the reference conditions used here which are 500 K and 10 atm. The units are : Temperature in Kelvin, Pressure in atmosphere, H 2 ratio in mol% of air and the Exhaust Gas Recirculation (EGR) in mol%. This equation combines previously established mathematical functions to model the laminar flame speed trends for hydrocarbons versus temperature, pressure, λ or EGR (32; 33; 34). The combined modeling of hydrogen boost and EGR required an additional term being a function of both parameters. The hydrogen effect of the fuel laminar flame speed in a diluted environment is indeed affected by the diluent composition but no data exists for this very specific case combining detailed EGR composition (i.e. N 2 , H 2 O and CO 2 ) and a TRFE. Hydrogen addition certainly increases the flame speed as it is shown in the litterature for different fuels and diluents such as iso-octane-H 2 /N 2 [START_REF] Tahtouh | Laminar premixed flame characteristics of hydrogen blended iso-octane air nitrogen mixtures[END_REF] or [START_REF] Duan | Laminar combustion characteristics and mechanism of hydrogen/air mixture diluted with N2 and H2O[END_REF]. However the quantitative effect predicted by the chemical mechanism and thus the correlation is uncertain and requires further work which is beyond the scope of this study. Consequently, care must be taken when considering this combined parameter variation as the quantitative effect is not validated. More than one thousand single calculations were performed and used to establish the correlation. The several parameters ranges considered are listed in Table 2 The absoluste flame speed deviation for the entire data is illustrated in Figure 4. It displays a deviation lower than 6.7 cm/s for flame speeds below 25 cm/s. For higher flame speeds, this highest deviation is 23% which is a data point on the edge of the considered parameters ranges. Consequently, the correlation provides a satisfactory agreement with the simulation results. Figure 5 illustrates the results obtained for the parameters tested and emphasizes the hydrogen effect simulated in the reference case studied here which is λ = 2.

Symbol

H 2 /N 2 - H 2 O

Auto-ignition modeling

The Tabulated Kinetics of Ignition (TKI) model (37; 38), accounts for auto-ignition in the frame of this study. It relies on tabulated auto-ignition parameters deduced from detailed chemistry calculations. In particular, the reaction rate due to auto-ignition is deduced from linear interpolation in a lookup table for local conditions of mean pressure, fresh gases temperature, fuel/air equivalence ratio, EGR volume fraction and auto-ignition progress variable. In order to capture the expected behaviors of a complexe gasoline fuel over a large set of thermodynamic conditions, a detailed kinetic mechanism validated for a gasoline surrogate fuel and for engine relevant conditions was used. The mechanism was developed by the Lawrence Livermore National Laboratory (LLNL) [START_REF] Mehl | Kinetic modeling of gasoline surrogate components and mixtures under engine conditions[END_REF] and simulates the autoignition delay of the selected species in this study. It includes about 6000 reactions and 1550 species. The hydrogen effect on the ignition delay for this mechanism has been discussed by Jain et al. [START_REF] Jain | Effect of hydrogen and syngas addition on the ignition of iso-octane and air mixtures[END_REF]. The later observed that the ignition delay increases compared to pure iso-octane below 1100 K. This auto-ignition delay increase is explained by an additionnal reaction pathway when hydrogen is added. The later reacts with OH radicals to produce water and this step slows the ignition process. Indeed, the hydrogen addition displays a large effect on the auto-ignition delay as demonstrated experimentally by An et al. [START_REF] An | The effects of hydrogen addition on the auto-ignition delay of homogeneous primary reference fuelair mixtures in a rapid compression machine[END_REF]. Rapid compression machine experiments from 735 K to 847 K indicate that both iso-octane and n-heptane auto-ignition delays were increased with hydrogen addition. These results highlight that the hydrogen boost depends both on λ and the fuel. This justifies the need for a dedicated kinetic model in order to account for these parameters. To illustrate the hydrogen effect with the TRFE, Figure 6 displays the autoignition delays for two λ values and three hydrogen levels considered in this study.

The increasing auto-ignition delay at lower temperature is indeed observed. In addition, a temperature limit at which hydrogen addition starts to display the opposite effect is . also confirmed based on the simulation performed by Jain et al. [START_REF] Jain | Effect of hydrogen and syngas addition on the ignition of iso-octane and air mixtures[END_REF]. The ignition delay decrease is related to two key reaction pathways at high temperature : O 2 + H = OH + O and H 2 O 2 + M = OH + OH + M. This negative effect of hydrogen should not have any impact on auto-ignition in lean SI engines since the fresh gases temperature should remain below 1000 K even when considering high compression ratios. To evaluate the boost effect on the ignition delay at a rather low temperature and for several equivalence ratios, a focus is made in Figure 7 at 700 K and 10 bar.

As λ increases, the ignition is further delayed for the same hydrogen level. This effect could be beneficial regarding knock as leaner mixture would contribute to limit the autoignition occurence in the engine. It is suggested that this would even be intensified for higher hydrogen levels.

The TKI-Table development was performed with an inhouse code named CLOE based on the Senkin solver from CHEMKIN II. More than 300,000 zero dimensionnal calculations were performed and their results stored to capture the progress variable that is related to the ignition delay. The parameters ranges considered are listed in

Engine overview and operating conditions

The engine used for both experimental and numerical investigations is characterized by a high stroke-to-bore ratio of 1.24, a high compression ratio of 14:1 and a high tumble motion of 2.4 combined with an Early Inlet Valve Closing (EIVC) timing strategy (see Table 4). This modern combustion system is fully representative of the most efficient serial production engines to date and has been thoroughly optimized for a high dilution resistance and high efficiency.

Cylinder Ignition is triggered with a classical spark-plug and a conventional ignition energy. However, it will be shown below that ignition can be performed efficiently under high pressure conditions and for ultra-lean mixtures thanks to hydrogen supplementation.

Impacts of H 2 on the combustion process have been both experimentally and numerically assessed for two operating points, summarized in Table 5. During the experimental testing phase, the dilution rate by air λ was varied for each of these operating points in order to quantify the impacts of hydrogen on the combustion stability limits with diluted mixtures (the maximal air dilution rate being reached when the coefficient of variation of IMEP exceeds 3%) and the minimal amounts of hydrogen required to reach λ = 2. Then, for the calculation phase, different topics were investigated depending on the load:

Speed IMEP λ ST SOI [rpm] [bar] [-] [CAD bTDC] [CAD
• At low load (2000 rpm -4 bar IMEP at λ = 2): the main objective was to highlight the positive impacts of H 2 on combustion speed with an optimal combustion timing. For this operating point, two calculations have been performed, with 3 v/v.% H 2 which is the minimal amount required to reach λ = 2, and without H 2 . In the following paragraphs, the amount of H 2 added at the intake is given as a fraction of the intake volume flow rate (air and hydrogen). Since a negative valve overlap was used for the tests, hydrogen was not directly scavenged towards the exhaust but remained in the combustion chamber. • At mid-load (3000 rpm -13 bar IMEP at λ = 2): the main objective was to highlight the positive impacts of H 2 on combustion speed and auto-ignition at similar non-optimal combustion timings. For this operating point, two calculations have been performed, with 3 v/v.% H 2 which is the minimal amount required to reach λ = 2, and without H 2 . It can be noted that the peak efficiency for the considered engine was obtained for this operating point during the experimental test campaign. 3D calculations performed for this operating point can thus also be used to numerically assess the heat balance in optimal conditions.

When they are available, experimental measurements are used as boundary conditions for numerical calculations (see Table 6). Without hydrogen, no experimental data was logically available at λ = 2 since this high dilution rate cannot be reached with a classical ignition system (usual limits for the considered operating points are of around 1.6 to 1.7).

Speed [rpm] IMEP [bar] H

2 [v/v.%] λ [-] Exp.

CONVERGE simulations

The 3D-RANS simulations presented in the framework of this paper were performed using the solver CONVERGE (42; 43). Combustion is modeled by the Extended Coherent Flame Model (ECFM) model (44; 45). It relies on a flame surface density equation which takes into account the wrinkling of the flame front surface by turbulent eddies. To represent physical phenomena describing ignition, the ISSIM model [START_REF] Truffin | A spark ignition model for large eddy simulation based on an FSD transport equation (ISSIM-LES)[END_REF], adapted for RANS formalism, is used. The model is divided into two parts: after an initial burned gases deposit, the flame kernel growth is modeled thanks to a modified flame surface density equation. The ignition data is obtained through the look up table or TKI-table discussed earlier.

Concerning turbulence, the k-ǫ RNG model is used, along with a law-of-the-wall approach (47) to model the turbulence kinetic energy and dissipation within boundary layer. Thermal heat fluxes at the wall boundaries are calculated using the wall heat transfer model of O'Rourke and Amsden [START_REF] Amsden | KIVA-3V: A Block-Structured KIVA Program for Engines with Vertical or Canted Valves[END_REF].

The liquid injection is modeled using the blob injection approach (49): the characteristic size of the injected parcels are equal to the effective diameter of the nozzle. The Kelvin-Helmhotz (KH) and Rayleigh-Taylor (RT) instability mechanisms are used to model the spray breakup [START_REF] Senecal | A New Parallel Cut-Cell Cartesian CFD Code for Rapid Grid Generation Applied to In-Cylinder Diesel Engine Simulations[END_REF]. The interactions between spray and walls rely on a particle-based approach. In particular, the liquid film transport follows the model of O'Rourke and Amsden [START_REF] Rourke | A Spray Wall Interaction Submodel for the KIVA-3 Wall Film Model[END_REF].

The gaseous computational domain is automatically discretized using the cut-cell Cartesian method implemented in CONVERGE. In the whole domain, mesh size is equal to 2 mm and some regions are locally refined to capture physics phenomena. To ensure sufficient grid refinement during the combustion phase, fixed embedding are used in the liquid spray region and along the walls (see Figures 8b and9b). The mesh size near the wall is around 0.25 mm. During the combustion phase, a refined region is defined around the spark plug where the mesh size is equal to 0.5 mm. In addition, the Adaptive Mesh Refinement tool is also used to apply a local refinement of the grid in the regions of strong gradients of velocity and temperature. This method enables to divide by two the mesh sizes in some regions, in particular during flame propagation or spray injection in order to capture respectively the flame front propagation and the spray dynamics.

3D Methodology to take into account H 2

Complex chemistry calculations in homogeneous reactors were first performed in order to quantify the impacts of H 2 on the laminar flame speed and on the auto-ignition delay times which are the two main characteristics that must be taken into account for an accurate modeling of the combustion process. From a numerical point of view, H 2 is taken into account in the laminar flame speed correlation These calculations aimed at showing that the TKI model is able to reproduce the same auto-ignition delay as those obtained with zero dimensional calculations performed in a previous section and the complex chemistry (that cannot be directly used for 3D CFD engine calculations because of computational time). Figures 10a and10b compare the temperature evolution between the complex chemistry and the 3D calculations for different λ. The 3D CONVERGE-TKI methodology is able to reproduce the auto-ignition of ultra-lean and stoichiometric mixture with and without H 2 supplementation. Figure 10a shows a weak increase of the auto-ignition delay for the 3D calculations because of the lower refinement of the TKI table for this range of equivalence ratio. These academic cases show however that H 2 effect on auto-ignition can be well reproduced for 3D engines calculations.

Numerical investigations on H 2 enhanced combustion

As shown in Table 6, some experimental data were not available since operating points at λ = 2 cannot be reached without H 2 supplementation because of the flammability limits of air/gasoline mixtures. The methodology used to simulate these operating conditions consisted in performing first the calculations with H 2 , and then in modifying the in-cylinder species field before the combustion, namely by replacing the hydrogen mass fraction by the same mass fraction of E10 surrogate and the global equivalence ratio remains constant. It must be noted that the combustion model have been fitted beforehand by comparison with experimental data at 2000 rpm, 18 bar IMEP, λ = 1 (not show in this article). Then, the same parameters have been used for calculations at λ = 2 (with and without hydrogen). The following analysis is performed on the second combustion cycle, to ensure that initial conditions are non-intrusive. TDC is located at 720 CAD for more clarity.

Low Load: 2000 rpm -4 bar

For this operating point, two kinds of comparisons can be performed. First, a comparison between numerical and experimental results obtained with 3 v/v.% of hydrogen can be performed at λ = 2. On the other hand, thanks to calculations results, it is also possible to compare the combustion process at λ = 2 with and without hydrogen.

Figure 11a compares the temporal in-cylinder experimental pressure with the numerical results obtained with and without hydrogen. Dashed lines in Figure 11a show the maximal cycle-to-cycle fluctuations for experimental results (over 300 cycles). A good agreement was found between experimental and numerical results obtained with H 2 , with similar combustion timings and similar peak firing pressures. This first observation shows that 3D software CONVERGE and its combustion models can be actually used to simulate the combustion process with hydrogen at λ = 2, even when these models are fitted at λ = 1. The average λ value is the same for calculations with and without hydrogen but the use of hydrogen clearly improves the combustion speed, through the laminar flame speed correlation. As shown in Figure 11a and Figure 11b, the use of hydrogen allows to burn all the air/fuel mixture in the cylinder whereas in the case without hydrogen, around half of the mixture is burnt. Such a combustion process could not be reproduced experimentally because the combustion speed is way too slow and the resulting cycle-to-cycle fluctuations would be too high. As it was found experimentally, 3D calculations show that the use of hydrogen is required to reach λ = 2 while maintaining an efficient combustion process.

To better understand why the combustion process is slowed down without hydrogen, Figure 12a shows the laminar flame speed integrated over the flame front for both cases with and without hydrogen. These results show for example that the maximal laminar flame speed is doubled when hydrogen is used (from around 0.45 m/s up to 0.9 m/s). It can also be noted that this maximal value of around 0.9 m/s is in the same order of magnitude of what could be expected at λ = 1 without any dilution for the considered pressure and temperature conditions.

Coherently with the laminar flame speeds shown in Figure 12a, rates of heat release (including premixed combustion and auto-ignition) plotted in Figure 12b show that the combustion speed is increased with hydrogen. It is interesting to note that critical combustion instabilities can be usually experimentally observed when the maximal rate of heat release is lower than 0.04 CAD -1 which is the maximal value that is predicted here by 3D calculations without hydrogen. It is shown for example by de Francqueville and Michel with EGR at high load [START_REF] De Francqueville | On the Effects of EGR on Spark-Ignited Gasoline Combustion at High Load[END_REF], and it can also be derived from the results reported in lean conditions by Hanabusa et al. [START_REF] Hanabusa | Study on Homogeneous Lean Charge Spark Ignition Combustion[END_REF]. The minor peak of heat release rate occurring with hydrogen around 730 CAD is due to a weak auto-ignition process (heat released by auto-ignition being here far below the usual threshold for detecting knock, confirming thus of course that no knock occurs for this low load operating point). This flame speed variation illustrates the positive effect on hydrogen on the combustion process but it is also interesting to focus on the beginning of combustion by analyzing the angular duration between spark timing and the CA05 angle (crankshaft angle for which 5% of the fuel is burnt). With hydrogen, this angular duration is of around 17 CAD, and it increases up to 26.7 CAD without hydrogen (56% increase). This improvement shows that hydrogen can enhance not only the maximal combustion speed, but also the flame growth just after the spark at the very beginning of combustion which is a critical aspect of combustion with diluted mixtures. also compares the crankshaft angles for which 10 and 50% of the fuel is burnt for experimental results and for the calculations results with hydrogen. Overall, a good agreement between experimental and numerical results can be observed. The optimal combustion timing CA50 was set at the test bench at around 7 CAD aTDC and a similar but slightly advanced combustion timing is obtained with 3D calculations performed with the same spark-timing as for the experimental tests. The same observation can be made for the CA10 angles: 3D calculations show a slightly earlier CA10 than the experiments. In fact, the duration of the first half of combustion is exactly the same for experiments and calculations, but the combustion process predicted by calculations occurs slightly earlier than for the experiments (around 2 CAD earlier). From 25 CAD after ST, Figures 14c,14d, 14e and 14f show that the flame front propagates steadily in the combustion chamber, suggesting that the flame speed around the flame front is constant. It could be explained by a very good mixture homogeneity, but as shown in Figure 15, the λ field before spark timing shows some heterogeneities around the spark-plug. This observation suggests that the H 2 homogeneous distribution allows to compensate the gasoline heterogeneities, leading to a more spherical development of the flame front.

Mid Load: 3000 rpm -13 bar

The engine speed and loads are higher than for the first operating point, but similar conditions are considered at λ = 2, with 3 v/v.% of hydrogen or without hydrogen, and for an optimal knock-free combustion timing. Figure 16a compares the temporal in-cylinder experimental pressure with the numerical results obtained with and without hydrogen. Dashed lines are plotted to show the experimental cycle-tocycle fluctuations (300 cycles). As for the previous operating point, 3D calculations can well reproduce the combustion process obtained with hydrogen with similar combustion timing and peak firing pressure as for experimental results. As show by Figure 16b, only a very slow combustion occurs when hydrogen is replaced by the E10-surrogate fuel, and only half of the fuel is burnt.

As for the previous operating point, the maximal laminar flame speed is doubled with hydrogen, from 0.25 up to 0.5 m/s (see Figure 17a). Figure 17b shows that the turbulent combustion speed is also significantly increased with hydrogen and a short combustion duration can thus be obtained even at λ = 2. It is interesting to note here again that the maximal rate of heat release without hydrogen is of around 0.03 CAD -1 . Parasitic oscillations observed between 730 and 740 CAD without hydrogen are caused by the numerical integration process which is observed only if the combustion process is significantly slowed down.

The angular duration between spark-timing and the CA05 angle is also significantly reduced thanks to hydrogen, from 31.7 CAD down to 17.5 CAD. The initial flame growth is strongly improved knowing that in both cases the ignition model and the turbulence levels are exactly the same. 18 shows that the duration of the first half of combustion is slightly lower for calculations as for the experimental results. The CA10 angles are the same, but the combustion timing CA50 obtained with calculations is slightly earlier than for the experiments (as for the first operating point, calculations have been performed with the same spark-timing as for the experiments).

Figure 19 illustrates the temporal evolution of the reaction rates related to the premixed flame propagation and to autoignition for the calculation performed with hydrogen. As shown, the major part of heat release is due to the flame front propagation and only a small part of the fresh gases is concerned by auto-ignition at the end of combustion. Indeed, the auto-ignition level is of around 4.8% despite the slightly earlier combustion timing than for experiments. This autoignition level is defined as the ratio between the heat released by auto-ignition, and the total heat released and the usual criterion used to define the knock limited spark-timing is 5%. According to the experimental results, it was possible to reach an almost optimal combustion timing CA50 of around 8 CAD aTDC, which means that knock was not a significant issue despite the load.

Figure 20 shows a 3D visualization of the flame front development and auto-ignition. These fronts are respectively represented by iso-surfaces of the ECFM progress variable (colored in grey) and the TKI progress variable (colored in black). No auto-igntion occurs during the first part of the combustion process, as shown in Figures 20a and20b, which is in agreement with results shown in Figure 19. Figures 20c and20d show then the potential auto-ignition zones in the combustion chamber. Because of the fuel consumption by the flame propagation, no auto-ignition is occurring in theses zones, where temperature is high. It explains why AI is located behind the flame front propagation in Figures 20c and20d. The AI progress variable only highlights potential AI areas but as the fuel has already been consumed, no AI occurs behind the flame front. However, the weak increase of the auto-ignition reaction rate observed in Figure 19 around 737 CAD can also be observed in Figure 20d where autoignition and flame propagation are interwoven (under one of the exhaust valves).

Eight operating points were experimentally investigated and the maximal efficiency was obtained for these operating conditions at 3000 rpm, 13 bar IMEP, and at λ = 2. Heat balances for experimental and numerical results cannot be directly compared because different models and methodologies are used to quantify these heat balances in both cases. However, it is interesting to note that similar orders of magnitude are obtained. For this operating point, the total heat loss inside the combustion chamber (excluding thus intake and exhaust ports) was close to 12% according to the experimental results, and is here close to 10.8% based upon 3D calculation results (around 128 J, 36% for the piston, 34% for the cylinder head, and 30% for the liner, see Figure 21). In parallel, a maximal indicated efficiency of 47.3% was obtained during experiments while the indicated efficiency calculated based upon 3D calculations results is of 48.5%. This higher efficiency is, however, consistent with the earlier combustion timing (see Figure 18) and with the higher combustion speed obtained through calculations (see Figure 17b, 0.055 CAD -1 and 0.046 CAD -1 for numerical and experimental results respectively).

Conclusion

The main objective of this study was to perform 3D CFD engine calculations in order to numerically analyze the hydrogen effects by comparing numerical results with experimental data. To do so, the effects of hydrogen on the laminar flame speed and on auto-ignition delay times have first been evaluated through chemical kinetics calculations. The new laminar flame speed correlation and tabulated auto-ignition delay times have been implemented in the CONVERGE CFD code and validated thanks to reference test cases. Then, the positive impacts of hydrogen have been numerically demonstrated for two different operating points at λ = 2 through 3D CFD engine calculations. Thanks to the updated ECFM and TKI models, 3D engine calculations firstly show that the experimental combustion process observed at λ = 2 can be numerically reproduced which is already a significant achievement since these conditions are very far from the usual operating conditions of SI engines.

Moreoever, thanks to these numerical investigations, it is also possible to analyze more precisely the positive effects of hydrogen at λ = 2. At low load, the homogeneous hydrogen concentration supports a nearly spherical flame propagation despite the air/gasoline ratio heterogeneities. At mid load, hydrogen demonstrated not only a positive effect on the combustion velocity but also as expected on the auto-ignition resistance. No variation has been performed regarding the amount of hydrogen used to enhance the combustion process since one of the main effect expected for this kind of variation is the improvement in combustion stability, which cannot be analyzed through RANS calculations. In this context, Large-Eddy Simulations could be interesting to determine the minimal amount of hydrogen to reach a targeted λ level.

Figure 1 .

 1 Figure 1. Modeling of the laminar flame speed of pure H2 in air versus pressure and for two equivalence ratios. Experimental data were collected from Shebeko et al. (24), Iijima and Takeno (25) and Qin et al. (26).

Figure 2 .

 2 Figure 2. Modeling of the laminar flame speed of pure H2 in air versus λ for three temperatures: 298 K, 373 K and 443 K. The experimental data, extracted from Krejci et al. (28), are represented in symbols and the new correlation in lines.

Figure 3 .

 3 Figure 3. Modeling of the laminar flame speed boost for the fuel surrogate used in this study compared to the measured boost effect on iso-octane at 443 K and λ = 1.25 (29).

Figure 4 .

 4 Figure 4. Absolute deviation obtained for the entire dataset used to establish the laminar flame speed correlation. The solid and dashed black lines represent respectively the deviation of 5% and 10%.

Figure 5 .

 5 Figure 5. Illustrations of the laminar flame speed correlation results for parameters variations including λ, temperature, pressure and dilution.

Figure 6 .

 6 Figure 6. Ignition delay modeling in a closed batch reactor under constant volume, 10 bar and no dilution for λ = 1 (grey)and 2 (black) and three hydrogen levels : 0 (fuel surrogate), 2 and 4 v/v.%. The hydrogen level is expressed in volumetric percents of air.

.

Figure 7 .

 7 Figure 7. Ignition delay decrease up to 4 v/v.% of hydrogen added to air at 700 K, 10 bar and no dilution for three λ : 1, 1.3 and 2.

Figure 8 .

 8 Figure 8. 3D visualization of the injection phase, 43 CAD after SOI.

Figure 9 .

 9 Figure 9. 3D visualization of the combustion phase, 19 CAD after ST.

Figure 10 .

 10 Figure 10. Impact of H2 on auto-ignition in homogeneous reactor (40 bar, 900 K without dilution).

Figure 11 .

 11 Figure 11. Comparison of experimental and numerical in-cylinder pressure evolution (a) and integrated heat release with and without hydrogen (b).

Figure 12 .

 12 Figure 12. Evolution of the laminar flame speed integrated over the flame front (a) and the rate of heat release (b) with and without hydrogen.

Figure 13 .

 13 Figure 13. Comparison of CA10 and CA50 for experimental and numerical results with H2.

(a) 15 Figure 14 .

 1514 Figure 14. 3D visualization of the flame front propagation (CFM progress variable = 0.99). The dashed line represents a fictive spherical propagation.

Figure 15 .

 15 Figure 15. Visualization on a central plane of the combustion chamber of the λ field, 1 CAD before ST.

Figures 14

 14 Figures 14 shows a 3D visualization of the the flame front development for six crank angles after the spark timing. The flame front is represented by an iso-surface of the ECFM progress variable (set to 0.99) and a dashed line represents a theoretical spherical flame front development. From 25 CAD after ST, Figures14c, 14d, 14e and 14f show that the flame front propagates steadily in the combustion chamber, suggesting that the flame speed around the flame front is constant. It could be explained by a very good mixture homogeneity, but as shown in Figure15, the λ field before spark timing shows some heterogeneities around the spark-plug. This observation suggests that the H 2 homogeneous distribution allows to compensate the gasoline heterogeneities, leading to a more spherical development of the flame front.

Figure

Figure 16 .

 16 Figure 16. Comparison of experimental and numerical in-cylinder pressure evolution (a) and integrated heat release with and without hydrogen (b).

Figure 17 .

 17 Figure 17. Evolution of the laminar flame speed integrated on the flame front (a) and the rate of heat release (b) with and without hydrogen.

Figure 18 .

 18 Figure 18. Experimental and numerical comparison of characteristic combustion timings.

Figure 19 .

 19 Figure 19. Comparison between the reaction rate of the premixed flame and the auto-ignition for the case at λ = 2 with 3 v/v.% of hydrogen.

(a) 16 Figure 20 .

 1620 Figure 20. 3D visualization of the flame front propagation (CFM progress variable = 0.99, represented in grey) and the auto-ignition location (TKI progress variable = 0.99, represented in black).

Figure 21 .

 21 Figure 21. Global energy distribution of the combustion system.

Table 1 .

 1 Constants for the laminar flame speed correlation.

		Constant
	a	0.04703
	b	1.0653
	c	36476.8183
	d	5.7836
	e	-1.2436
	f	1.2857
	h	2.)
	j	-0.03584
	k	0.00016
	l	-0.032399
	m	-0.00058

3139+2.7353((Φ -1) 2 ) -0.58956(Φ -1) i -0.33945-0.021676((Φ -1) 2 ) + 0.13269(Φ -1

  . It should be emphasized that while this study aims to simulate a lean combustion, the correlation must provide reasonable values for rich mixtures as heterogeneities are present in 3D calculations.

	Parameters	Min. Max.
	H 2 in air intake (v/v.%)	0	6
	Temperature (K)	500	850
	Pressure (bar)	10	120
	λ (-)	0.58	2
	EGR (mol/mol%)	0	10

Table 2 .

 2 Parameters ranges considered for the developed laminar flame speed correlation.

Table 3 .

 3 Those are different than the ones presented for laminar flame speed calculations as the ignition time evaluation implies to consider the system history for which large parameters variations are encountered.

	Parameters	Min. Max.
	H 2 in air intake (v/v.%)	0	6
	Temperature (K)	500 1500
	Pressure (bar)	1	120
	λ (-)	0.3	3.3
	EGR (mol/mol%)	0	10

Table 3 .

 3 Parameters ranges considered for the developed auto-ignition table.

Table 4 .

 4 Engine characteristics.

	Displacement	[cm 3 ]	410.9
	Valves	[-]	4
	Stroke	[mm]	93.0
	Bore	[mm]	75.0
	Compression ratio	[-]	14:1
	Tumble level	[-]	2.4
	Intake valve lift	[CAD]	140
	EVC/IVO @1 mm lift [CAD aTDC]	-10 / 10
	Injection	[-]	Central direct

Table 5 .

 5 Investigated operating points.

	bTDC]

Table 6 .

 6 Available experimental data for numerical calculations.

	data

Prepared using sagej.cls

Acknowledgement

The project leading to this application has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 724084

A prediction study of the effect of hydrogen blending on the performance and pollutants emission of a four stroke spark ignition engine. International Journal of Hydrogen Energy 1999; 24(4): 363-375.


Development of a method to evaluate odour quality based on non-expert analysis

Keywords: Odour Quality, Natural Language, Information Fusion, Taxonomy, Semantic Proximity

Characterizing odour quality is a complex process that consists in identifying a set of descriptors that best synthesizes the olfactory perception. Generally, this characterization results in a limited set of descriptors provided by professionals in sensorial analysis. These experts previously learnt a common language to describe characteristic odour (Odour wheel or Champ des odeurs © ). These sensorial analysis sessions cost industrial manufacturers large sums every year. If this characterization is entrusted to neophytes, the number of participants of a sensorial analysis session can be significantly enlarged while reducing costs. However, each individual description is no more related to a set of nonambiguous descriptors but to a bag of terms in natural language. Two issues are then related to odour characterization. The first one is how translating free natural language descriptions into structured descriptors; the second one is how summarizing a set of individual characterizations into a consistent and synthetic unique characterization for professional purposes. This paper will propose an approach based on natural language Processing and Knowledge Representation based techniques to formalize and automatize both translation of bags of terms into sets of descriptors and summarization of sets of structured descriptors.

Introduction and problem

Odours represent a very important issue for societal and industrial perspective and activities due to the intrinsic character of the odour, or to the frequency of the perception [START_REF] Gostelow | Sewage treatment works odour measurement[END_REF]. In general, the industries' locations or the sale of materials for building and furnishing, household care products depend of their odour acceptability in the neighbourhood or by potential buyers. Because of around of the European population are annoyed by environmental odours, the rules and the regulations have been enhanced in the odour monitoring's field [START_REF] Jorf | Arrêté du 12 / 02 / 03 relatif aux prescriptions applicables aux installations classées soumises à autorisation sous la rubrique 2730 ( traitement des cadavres , des déchets ou des sous-produits d' origine animale à l'exclusion des activités visées par[END_REF], [START_REF] Belgiorno | Odour Impact Assessment Handbook[END_REF].

The characterization of the odour quality consists in the verbalization of the perception that a person makes of his feeling based on his experience and his knowledge. Currently, the evaluation of odours quality is commonly made through controlled linguistic descriptors provided by trained experts. Industrials have developed specific classifications to calibrate the quality of odours. We can mention the Champ des odeurs © for perfumers [START_REF] Jaubert | The field of odors: toward a universal language for odor relationships[END_REF] and the wheel of odours for example for oenology [START_REF] Noble | Progress Towards a Standardized System of Wine Aroma Terminology[END_REF] or drinkwater [START_REF] Suffet | The anatomy of odour wheels for odours of drinking water , wastewater , compost and the urban environment[END_REF]. By using a common referential to qualify odours, these methods facilitate understanding, interpreting and results processing. Nevertheless, a learning phase in which valid descriptors have to be learned is required to use such methods. This (i) prevents their use by non-experts, (ii) implies additional training costs, and (iii) limits the number of evaluators and experiments that can be used to evaluate odours.

Figure 1: Process of the odour quality evaluation

A natural language (NL) is a language spoken by individuals without prior training. It is a vocabulary that is opposed to the language of experts. The characterization of odour quality by non-expert evaluators is far from a socalled objective description because they used terms referring to sources or pleasantness. On the contrary, experts seem to forget the hedonic tone of the odours when they characterize quality [START_REF] Sezille | Hedonic appreciation and verbal description of pleasant and unpleasant odors in untrained, trainee cooks, flavorists, and perfumers[END_REF].

The aim of our project is to propose a method that enables the transcription of free descriptions given in natural language into structured descriptors.

Introductory example:

Let's consider that we have two non-expert evaluators to describe the odour of a given tested wine, each of their free descriptions is related to a bag of terms in natural language. Defining a method to merge the information expressed by the free descriptions that is useful for professionals necessitates a good deal of knowledge of the domain-specific descriptors and their taxonomical organization. If for example, we consider that we get the following conceptual descriptions and after translating the free NL descriptions into structured descriptors:

The idea is to merge the information expressed by these two conceptual annotations and to formally characterize the odour of this wine by a unique synthetic of concepts. Intuitively, abstracting these two descriptions by summarizing the information provided by the descriptors relies on our knowledge of the organization of the descriptors. Because there is no formal definition to what a relevant summary is, several summaries can be proposed, e.g. Indeed, the idea beyond this choice is that and are sorts of (i.e. ), is a sort of and is a specification of . This reasoning is based on taxonomic knowledge partially ordering concepts (descriptors).

The next section presents the proposed method, we compute the individual conceptual annotation and then a formal method to produce the conceptual summary that best approximates all the annotations.

Method:

In this article, we consider that the terms used by nonspecialists to describe the quality of an odour are free: it is assumed that the evaluators use their own terms to describe the odour. Therefore, the terms we have to deal with are not part of a controlled vocabulary defining the descriptors commonly used for characterizing odours. The purpose of our work is therefore to identify the descriptors that are the most likely evoked by the natural language descriptions provided by non-experts. The notion of descriptor here refers to the concept used in Knowledge Representation (taxonomy). These concepts are assumed to be partially ordered into taxonomy , with the set of concepts [START_REF] Harispe | Semantic Similarity from Natural Language and Ontology Analysis[END_REF]. A concept is subsumed by a concept , i.e., if is more specific than and inversely is more imprecise than , e.g. Strawberry is a sort of Red fruit.

The first step of our method is to define a correspondence between terms and concepts of the related taxonomy. This mapping is based on a vectorial word-based representation that allows comparing, then associating terms and concepts. In this way, we compute the degree to which a term evokes a concept of the taxonomy. Then, each term of the NL description is associated the concept the term evokes the most. Finally the free description is transformed into a conceptual annotation. The second step is to synthesize conceptual annotations into a limited set of concepts. This step is all the more necessary when several evaluators provide their annotations to make the collective merged evaluation more synthetic and tractable.

In order to ease the readability of this section, the various definitions which will be used are listed below:

Notations:

 The notion of Information Content refers to the degree of specificity of concepts. [START_REF] Seco | An intrinsic information content metric for semantic similarity in wordnet[END_REF]. For any function must monotonically decrease from the leaves to the root of the taxonomy such as .  We denote by and respectively the inclusive ancestors and inclusive descendants of the set of concepts .

⋃ ⋃ , ⋃ ⋃
 We denote by the mass function, , that corresponds to the number of observations of concept and ∑ . In our application, represents the number of times concept has been proposed by evaluators.  The belief and plausibility functions and proposed in the Dempster-Shafer theory are next defined such as [START_REF] Harispe | On the consideration of a bring-to-mind model for computing the Information Content of concepts defined into ontologies[END_REF]:

∑ ∑
Step 1: Individual conceptual annotation

In this section, we try to associate to each term of a NL description, the concept that is the most likely evoked by the term. We intuitively propose to consider that a term evokes concepts (e.g. the term strawberry evokes with some degrees the concepts Red fruits and Fruits). The intensity of evocation a term has with a concept can be expressed as the semantic proximity between the term and the concept denoted by with the set of terms that constitute the vocabulary that has been previously established for the vectorial representation of words. We propose to use the distributional semantics models for evaluating term proximity with regard to their meaning (i.e. concepts). The distributional models are generally defined to capture the meaning of terms. These models are based on the distributional hypothesis which, in linguistics, states that words that are used and occur in the same contexts tend to purport similar meanings [START_REF] Harris | Distributional structure[END_REF]. The distributional semantic model is built from the distributional analysis of corpora. The first step to obtain such a model is to analyze terms co-occurrences in a large corpus (e.g. Wikipedia). These co-occurrences are then used to derive models. These models can be of various forms, e.g. word-word matrix [START_REF] Harispe | Semantic Similarity from Natural Language and Ontology Analysis[END_REF]. We next consider that measuring the similarity between a term and a concept returns to calculate the similarity between the term and the labels (terms) associated to the concept . The labels are provided in the input taxonomy. This semantic proximity between and can be estimated, for example, as the maximal of similarity between the term and the labels associated to the concept : with the set of terms associated to the concept and . Numerous measures for comparing terms have already been proposed in the literature [START_REF] Harispe | Semantic Similarity from Natural Language and Ontology Analysis[END_REF] , e.g. cosine measure. The objective of this step is to synthesize the information expressed by the terms of a description provided by evaluator to characterize the odour through a conceptual annotation. This conceptual annotation can be computed as follows:

⋃ (1)
This model considers a simple one-to-one correspondence between terms and concepts.

The next section focuses on the issue of summarizing the individual conceptual annotations in the case where we have many evaluators who have to characterize the same odour.

Step 2: Collective conceptual annotation (summary)

The aim of the study is to summarize the information given by a set of evaluators . We consider that each evaluator provides a set of terms to describe the odour. Using the model proposed in the previous section (Eq. ( 1)), a conceptual annotation can be associated to each individual bag of terms . The purpose of this section is to provide a synthesis (summary) that summarizes the information provided by evaluators. Indeed to each evaluator is now associated a set of concepts (individual conceptual annotation) denoted . In the following, we propose a method to summarize a set of individual conceptual annotations.

We denote by ̂=(

) the sequence of annotations to be summarized and the set of concepts issued from individual annotations such that: ⋃ . We suppose that each synthesis respects the following properties: 1) Summarizing: 2) Fidelity: such as x 3) Non-total-redundancy: ,

We denote by the set of summaries of a sequence of annotations ̂, each summary respects the three properties defined above. Based on these definitions, we formally define by the function summarizing a sequence of individual conceptual annotations by a single summary from : , with ̂ .

We define the problem of summarizing a sequence of annotations ̂ by finding , the best summary for ̂. The following section introduces the model that we propose for defining a function .

In order to define the best summary, we propose to define some notions that could be used to evaluate the relevance of a summary. The search of best summary for any ̂ can be expressed as an optimization problem. The objective function is defined as:

( ̂) ( ̂) ̂ ( ̂) ( ̂) ( ̂)
The function ( ̂) models the amount of information from ̂ covered by and ( ̂) is the penalty associated to the abstraction of ̂ by . The function ( ̂) is used to estimate the amount of conceptual information conveyed by ̂ which is summarized by . In this context, we are interested to measure the common abstract notions which are mentioned by ̂ and . That is to say, we search to evaluate the common information provided by the ancestors of ̂ and . Intuitively this quantity could be defined as follows:

( ̂ ) ∑

The function is introduced to weigh the importance of concepts. The function ( ̂ ) is illustrated in figure 2. The five concepts chosen to characterize odour quality are the best summary to explain the perception of all the panellists without forgetting crucial information.

These representations are very useful to qualify and compare olfactory perception for several products of a same production.

Conclusion

This new method is able to combine evaluation of odour quality from non-experts and to sort out a summary of concept. Works will be done on the validation of this method on a large scale of products and different perception. The evaluation of a taxonomy based on a multi-dimensional sensorial space might be useful to be as close as possible of human evaluation of odour quality.

Figure 2 :

 2 Figure 2: The dashed frame represents the set of concepts that directly contribute to compute ( ̂ ).

Figure 4 :

 4 Figure 4: Schematic representation of concepts from odour quality evaluation

In the following, we will detail the different factors of penalty. We define the penalty of abstraction ( ̂ ) by:

is the penalty associated to the deletion of the exact information. It models the amount of exact information conveyed by ̂ which is not conveyed by (e.g. concepts 1, 5, 6, 7, 8 in figure 3).

models the amount of plausible information conveyed by which is not conveyed by ̂ (penalty regarding addition of plausible information) (e.g. concept 9 in figure 3).

( ̂) ( ̂) ∑

models the amount of plausible information conveyed by ̂ which is not conveyed by (penalty regarding loss of plausible information) (e.g. descendants of concept 5 in figure 3).

( ̂) ( ̂) ∑

Finally, the aim of the penalty is to penalize the distortion which is made considering a specific choice among partially covering summaries. This penalty should be a function of i.e., all the elements of have not been summarized (e.g. concept 5 in figure 3). We propose the following model to estimate the distortion:

The parameter is used to weigh the importance of a specific uncovering (i.e. the concepts of that are not covered by ). Finally, the penalty of abstraction ( ̂ ) is defined as follow:

With

, , , input parameters used to set the importance of each abstraction penalty factor. These penalties are illustrated in figure 3. is a penalty used to evaluate the conciseness of the summary by penalizing redundant information implicitly conveyed by a summary.

∑

The penalization is designed such as each abstracted notion that is repeated more than once will be penalized the number of times the redundant information appears. The parameter can therefore be used to control the number of descriptors composing a summary. The more important , the more abstracted the summary.

Finally, the function ( ̂) can be used to express additional constraints over . This constraint can be used to apply specific restrictions on the type of solutions we are interested in. As an example, the requirement may be to keep only the covering summaries ( such as

).

Harispe et al. have proposed algorithms enabling to use the model for searching for relevant summaries and discuss interesting properties of the search space -details on the performances of these algorithms are provided in [START_REF] Harispe | Summarizing Conceptual Descriptions using Knowledge Representations[END_REF].

Experimental framework

In this example, fifteen members of a sensorial jury evaluated odour quality of wine. Each member described his perception with his own words.

For example, a member described the tested wine by the following words: black fruit, prune, strawberry, compote, cinnamon, peach, exotic fruit.

With the description all of the members, a bag of fifty seven different terms is obtained. When combining with our method, a summary of these terms in five concepts is represented in figure 4. The weight of each concept is figured on the axis. It reveals the importance of the concept in the bag of terms and, in fact, in the primary description of the perception.


Jean-Christophe Mensonides

Sébastien Harispe

Jacky Montmain

Véronique Thireau
email: veronique.thireau@unimes.fr

Automatic Detection and Classification of Argument Components using Multi-task Deep Neural Network

In this article we propose a novel method for automatically extracting and classifying argument components from raw texts. We introduce a multi-task deep learning framework exploiting weight parameters trained on auxiliary simple tasks, such as Part-Of-Speech tagging or chunking, in order to solve more complex tasks that require a fine-grained understanding of natural language. Interestingly, our results show that the use of advanced deep learning techniques framed in a multi-task setting enables competing with state-of-the-art systems that depend on handcrafted features.

Introduction

Argumentation consists in a set of methods aiming at making an interlocutor adhere to an introduced point of view or conclusion -or at least to increase its adherence to the latter. In its simplest form, argumentation is a reasoning process selecting and structuring premises to attack or defend a given conclusion. Although the study of argumentation is well established in fields such as Logic, Philosophy, or Linguistics, automatic extraction and analysis of arguments from natural language, commonly referred as Argument Mining, is a relatively new research area. Advances in Argument Mining are of major importance for Natural Language Understanding and Processing, in particular due to their several direct applications and relationships with other tasks, e.g. fake news detection, knowledge base enrichment and population, source trustworthiness estimation. To date, most advanced argument mining systems aims at generating argument-based graphs identifying and structuring premises, claims and conclusion from raw texts [START_REF] Cabrio | Five years of argument mining: a data-driven analysis[END_REF]. They can usually be split into sequences of subtasks including argument detection and argument linking [START_REF] Lippi | Argumentation mining: State of the art and emerging trends[END_REF]. This article focuses on analyzing argumentative micro-structure, i.e., how different argumentative components interact with each other within a single text.

Three types of argument components are often used in the annotation scheme considered in Argument mining: (i) major claims, reflecting the author's standpoint on the debated topic, (ii) claims, which are statements needing further justifications to be accepted, and (iii) premises, which are justifications used in order to make a claim stand [START_REF] Stab | Parsing argumentation structures in persuasive essays[END_REF]. Those components are further structured into a directly acyclic graph in which nodes account for argumentative components and edges account for oriented links between them. Directed edges in the graph are labeled either as a support or attack relationship, and are only allowed a) from a premise to another premise, b) from a premise to a (major) claim, and c) from a claim to a (major) claim. Such an annotation schema is adopted by [START_REF] Stab | Parsing argumentation structures in persuasive essays[END_REF] in the Argument Annotated Essays corpus (version 2) composed of 402 manually annotated student essays taken from essayforum.com.

A pipeline of treatments is generally applied to automatically obtain the graph reflecting the underlying argumentative structure of an essay. The following intuitive decomposition involving four subtasks is moreover often considered in practice [START_REF] Stab | Parsing argumentation structures in persuasive essays[END_REF]: (1) argument components identification, (2) argument components classification, (3) assessment of the existence of directed edges between argument components, and (4) tagging of the existing directed edges either as a support or as an attack relation-ship. This article will focus on solving subtasks (1) and (2). It presents a novel multi-task approach to argument mining that does not require handcrafted features as input. We are in particular interested by evaluating if recent deep learning techniques, such as recurrent neural network mixed with multi-task learning, can compete with traditional approaches based on handcrafted features.

The paper is organized as follows. Section 2 introduces an overview of related work proposed on tasks similar to (1) and (2). Section 3 introduces our approach to solve those two tasks. Section 4 describes the training details of the proposed model. Section 5 describes our experiments and results. Section 6 provides directions and perspectives for future works.

Related work

Argument components detection consists in determining the boundaries separating the textual units carrying arguments from the rest of the text. This task is generally considered as a supervised text segmentation problem at word level. Models exploiting the sequential aspect of texts, inherent in the construction of a convincing argumentation, seem particularly adapted and are often used. [START_REF] Madnani | Identifying high-level organizational elements in argumentative discourse[END_REF] used a CRF (Conditional Random Field) to identify non-argumentative segments within dissertations. [START_REF] Levy | Context dependent claim detection[END_REF] identified the boundaries of textual units detailing conclusions which were supporting or attacking topics discussed in threads from Wikipedia. [START_REF] Ajjour | Unit segmentation of argumentative texts[END_REF] used LSTM (Long shortterm memory, recurrent neural network) to extract arguments from essays, editorials, and from user-generated comments. [START_REF] Goudas | Argument extraction from news, blogs, and social media[END_REF] first identified sentences containing arguments and then detected their boundaries within social media using a CRF. [START_REF] Sardianos | Argument extraction from news[END_REF] determined argument components boundaries in news articles using also using a CRF. Similarly, [START_REF] Stab | Parsing argumentation structures in persuasive essays[END_REF]) used a CRF to extract argument components in essays. [START_REF] Eger | Neural end-to-end learning for computational argumentation mining[END_REF] leveraged deep learning techniques to extract arguments from raw texts.

Determining the type of argument components (premise, conclusion, etc.) has often been treated as a supervised text classification problem. [START_REF] Eckle-Kohler | On the role of discourse markers for discriminating claims and premises in argumentative discourse[END_REF] distinguished premises and conclusions in news articles using Naive Bayes, Random Forest and SVM (Support Vector Machine). [START_REF] Park | Identifying appropriate support for propositions in online user comments[END_REF]) also used a SVM to determine the extent to which claims are justified in citizen's comments related to possible new legislation projects. [START_REF] Stab | Parsing argumentation structures in persuasive essays[END_REF] classified argumentative components into premises, claims and major claims in essays using a SVM. [START_REF] Persing | End-to-end argumentation mining in student essays[END_REF] used maximum entropy classification to determine the type of argument components. [START_REF] Potash | Here's my point: Joint pointer architecture for argument mining[END_REF] used sequence-to-sequence recurrent neural networks to infer the type of argument components.

Multi-tasks models are able to handle several different problems by sharing a subset of shared parameters. They have been subject to recent interest within the Natural Language Processing community [START_REF] Hashimoto | A joint many-task model: Growing a neural network for multiple nlp tasks[END_REF][START_REF] Søgaard | Deep multi-task learning with low level tasks supervised at lower layers[END_REF][START_REF] Eger | Neural end-to-end learning for computational argumentation mining[END_REF][START_REF] Yang | Multi-task cross-lingual sequence tagging from scratch[END_REF]. This type of models is bio-inspired: human beings are able to carry out a multitude of different tasks and can exploit, when necessary, knowledge related to different types of problems, making the learning of new tasks faster and easier. [START_REF] Ruder | An overview of multi-task learning in deep neural networks[END_REF] states the reasons why this type of model is effective from a machine learning point of view: the use of several different corpora induces an implicit increase in the number of examples available during the training phase. In addition, the model has to look for characteristics which may be useful for all the tasks to be processed, which limits the noise modeling and thus, leads to a better generalization. [START_REF] Søgaard | Deep multi-task learning with low level tasks supervised at lower layers[END_REF] showed that inducing a priori knowledge in a multi-task model, by ordering the tasks to be learned, leads to better performance. [START_REF] Yang | Multi-task cross-lingual sequence tagging from scratch[END_REF] have shown that driving a multi-task and multi-language model can improve performance on problems where data is only partially annotated. [START_REF] Hashimoto | A joint many-task model: Growing a neural network for multiple nlp tasks[END_REF] obtained competitive results on several different tasks with a single model. However, we should note that there is no guarantee on the benefits of using multi-task models, and that their success depends on the data distribution related to the various problems treated [START_REF] Mou | How transferable are neural networks in nlp applications[END_REF][START_REF] Martínez | When is multitask learning effective? semantic sequence prediction under varying data conditions[END_REF][START_REF] Bingel | Identifying beneficial task relations for multi-task learning in deep neural networks[END_REF]. [START_REF] Schulz | Multitask learning for argumentation mining in lowresource settings[END_REF] proposed a multi-task framework to perform end-to-end argument mining. The result they obtained are very promising. In this paper, we are interested in leveraging auxiliary informa-

Proposed approach

We propose a model which aims at 1) determining argument components boundaries within a set of essays and 2) determining the type of each argument component within the latter essays. We draw inspiration from the work of [START_REF] Hashimoto | A joint many-task model: Growing a neural network for multiple nlp tasks[END_REF] and opt for a multi-task model without the definition of handcrafted features. Specifically, we use deep learning techniques and develop a model that performs Part-Of-Speech (POS) tagging, chunking, argument components boundaries detection, and argument components classification. An overview of the model architecture is given in Figure 1. The different layers used in the architecture are described below.

Word embeddings

We first use a word embedding layer, assigning a vector representation e t to each word w t given in input to the system. We use GloVe [START_REF] Pennington | Glove: Global vectors for word representation[END_REF] to obtain a set of pre-trained embeddings in an unsupervised fashion 1 . Note that, word embeddings are continually optimized while training the model on the different tasks described below. Out-of-vocabulary words are mapped to a special <UNK> token. 1 We used pre-trained embeddings from https://nlp. stanford.edu/projects/glove/.

POS tagging

The second layer of the model corresponds to a POS tagging task, consisting in assigning a POS tag (noun, verb, adjective, etc.) to each word w t given in input to the system. We use a bi-directional Gated Recurrent Unit (GRU) [START_REF] Cho | Learning phrase representations using rnn encoder-decoder for statistical machine translation[END_REF] to encode input word sequences.

GRU is a recurrent neural network using a gating mechanism and avoiding the use of a separate memory cell. At each time step t, GRU computes the hidden state h t as follows:

h t = (1 -z t ) * n t + z t * h (t-1)
with

n t = tanh(W n x t + b n + r t * (W hn h (t-1) + b hn )) r t = σ(W r x t + b r + W hr h (t-1) + b hr ) z t = σ(W z x t + b z + W hz h (t-1) + b hz )
where x t is the input at time step t, r t , z t and n t are respectively the reset, update and new gates, σ is the sigmoid function, and W and b are matrice and vector parameters.

In order to exploit the past and future contexts of an element from a sequence of N inputs [x 1 , x 1 , ..., x N ], we construct a bi-directional encoding by concatenating the hidden states obtained with a forward encoding (e.g, at time step t = 1, the input is x 1 , at time step t = 2, the input is x 2 , etc.) and a backward encoding (e.g, at time step t = 1, the input is x N , at time step t = 2, the input is x N -1 , etc.):

- → h t = ---→ GRU (x t ), t ∈ [1, N ] ← - h t = ← --- GRU (x t ), t ∈ [N, 1] h t = [ - → h t ; ← - h t ]
We use word embeddings as input of the POS tagging layer:

--→ h (1) t = ---→ GRU (e t ) ← -- h (1) t = ← --- GRU (e t ) h (1) t = [ --→ h (1) t ; ← -- h (1) t ]
Then for each time step t we compute the probability of assigning the label k to the word w t as follows:

p(y (1) t = k|h (1) t ) = exp(W sm (1) f c (1) t + b sm (1) ) c 1 exp(W sm (1) f c (1) t + b sm (1) ) (1) f c (1) t = relu(W f c (1) h (1) t + b f c (1) ) (2)
where W and b are parameter matrices and vectors, ReLU is the Rectified Linear Unit function [START_REF] Nair | Rectified linear units improve restricted boltzmann machines[END_REF], and c 1 is the set of possible POS tags.

Chunking

Chunking is a task aiming at assigning a chunk tag (noun phrase, verb phrase, etc.) to each word. We compute hidden chunking states by exploiting what the model has learned for the POS tagging task:

--→ h

(2)

t = ---→ GRU ([e t ; h (1)
t ; y

(P OS) t ]) ← -- h (2) t = ← --- GRU ([e t ; h (1)
t ; y

(P OS) t ]) h (2) t = [ --→ h (2) t ; ← -- h (2) t ]
where h

(1) t

is the hidden state obtained at time step t on the POS tagging task and y (P OS) t is the weighted POS label embedding. Following [START_REF] Hashimoto | A joint many-task model: Growing a neural network for multiple nlp tasks[END_REF], y (P OS) t is defined as follows:

y (P OS) t = card(c 1 ) j=1 p(y (1) t = j|h (1) t )l(j) (3)
where l(j) is an embedding of the j-th POS tag. POS tag embeddings are pre-trained using GloVe.

The probability to assign a chunk tag to each word is then computed in a similar way to the one for POS tags (eq. ( 1) and ( 2)), but with a set of parameters specific to the chunking layer.

Argument Components Detection (ACD)

Argument components detection aims at delimiting the boundaries of each argument component within essays at the word level. We follow [START_REF] Stab | Parsing argumentation structures in persuasive essays[END_REF] and treat this task as a supervised text segmentation problem, where labels follow an IOB-tagset (Ramshaw and Marcus, 1999): the first word of each argument component carries an "Arg-B" label, remaining words of said argument component bear an "Arg-I" tag, and the words not belonging to any argument component bear an "O" tag.

Each essay is considered as a single word sequence which we encode as follows:

--→ h (3) t = ---→ GRU ([e t ; h (1) t ; y (P OS) t ; h (2) t ; y (chunk) t ]) ← -- h (3) t = ← --- GRU ([e t ; h (1) t ; y (P OS) t ; h (2) t ; y (chunk) t ]) h (3) t = [ --→ h (3) t ; ← -- h (3) t ]
where y (chunk) t is the weighted chunk label embedding, computed in a similar way as the one for POS labels (eq. ( 3)).

The probability to assign a chunk tag to a word is then computed in a similar way as the one for POS labels, but with a set of parameters specific the ACD layer.

Argument Components Classification (ACC)

Argument components classification aims at determining the type of each argument component between premise, claim and major claim. We treat this task as a segment labeling problem. We consider that a segment can be the sequence of words belonging to a same argument component or can be the sequence of words belonging to a same portion of continuous text whose words do not belong to an argument component. The notion of segment is illustrated in Figure 2. We encode each segment s i , i ∈ [1, L] as follows:

-→ h it = ---→ GRU ([e it ; h (1)
it ; y

(P OS) it ; h (2) it ; y (chunk) it ]) ← - h it = ← --- GRU ([e it ; h (1)
it ; y

(P OS) it ; h (2) it ; y (chunk) it ]) h it = [ -→ h it ; ← - h it ]
where it is the time step t for the segment s i .

[S1] The greater our goal is, the more competition we need. [S2] Take Olympic games which is a form of competition for instance, it is hard to imagine how an athlete could win the game without the training of his or her coach, and the help of other professional staffs such as the people who take care of his diet, and those who are in charge of the medical care.

[S3] The winner is the athlete but the success belongs to the whole team. Therefore [S4] without the cooperation, there would be no victory of competition [S5] . Consequently, no matter from the view of individual development or the relationship between competition and cooperation we can receive the same conclusion that [S6] a more cooperative attitudes towards life is more profitable in one's success. In order to help the model focusing on the most important markers (such as "I firmly believe that" or "we can receive the same conclusion that") we use an attention mechanism [START_REF] Bahdanau | Neural machine translation by jointly learning to align and translate[END_REF], which in addition allows us to synthesize the information carried by segments hidden states into fixed size vectors:

u it = tanh(W att h it + b att ) α it = exp(u it u att ) t exp(u it u att ) sh i = t α it h it
where W att , b att and u att are respectively parameter matrices, bias and vectors.

Then we encode each essay using the synthetic segments hidden states sh i :

--→ h (4) j = ---→ GRU (sh i ), i ∈ [1, L] ← -- h (4) j = ← --- GRU (sh i ), i ∈ [L, 1] h (4) j = [ --→ h (4) j ; ← -- h (4) j ]
The probability of assigning a label to each segment is then computed similarly to that for POS tags, but with a set of parameters specific to the ACC layer.

Model Training

For each epoch of training, we optimized the model's parameters for each layer. That is, at each epoch we trained the layers in the following order: POS tagging, chunking, ACD and ACC. In order to assess the relevance of implementing a multi-task model, we trained two versions of the model: a version where we trained every layer (referred as "w/ POS & chunking"), and a version for which we voluntary omitted to train the POS tagging and chunking layers (referred as "w/o POS & chunking"). The training details of each layer are described below.

POS tagging layer

Following [START_REF] Hashimoto | A joint many-task model: Growing a neural network for multiple nlp tasks[END_REF], we denote θ P OS = (W P OS , b P OS , θ e ) the set of parameters involved in the POS tagging layer. W P OS represents the set of parameter matrices for the POS tagging layer, b P OS the set of biases of the POS tagging layer, and θ e the set of parameters of the words embedding layer. The cost function is defined as:

J (1) = - s t log p(y (1) t = k|h (1) t ) +λ W P OS 2 + δ θ e -θ e 2
where p(y

(1) t = k|h (1)
t ) is the probability of assigning the right label k to the word w t of the word sequence s, λ W P OS 2 is the L2 regularization term and δ θ e -θ e 2 is a secondary regularization term. λ and δ are hyperparameters. The secondary regularization term aims at stabilizing the training by preventing θ e from being too specifically optimized to fit the POS tagging task. Indeed, since θ e is shared across all layers of the model, excessive modifications of its parameters would prevent the model from learning efficiently. θ e is the set of parameters involved in the word embedding layer at last epoch.

Chunking layer

We denote θ chunk = (W chunk , b chunk , E P OS , θ e ) the set of parameters involved in the chunking layer. W chunk et b chunk are respectively parameter matrices and bias of the chunking layer, including those of θ P OS . E P OS is the set of parameters characterizing the POS label embeddings. The cost function is defined as follows:

J (2) = - s t log p(y (2)
t = k|h (2) t ) +λ W chunking 2 + δ θ P OS -θ P OS 2 with p(y (2) t = k|h (2)
t ) the probability of assigning the right label k to the word w t of the word sequence s. θ P OS is the set of parameters of the POS tagging layer right before the training of the chunking layer for the current epoch.

ACD layer

We denote θ ACD the set of parameters involved in the ACD layer, with θ ACD = (W ACD , b ACD , E P OS , E chunk , θ e ). W ACD and b ACD are respectively parameter matrices and bias of the ACD layer, including those of the chunking and POS tagging layers. E chunk is the set of parameters characterizing the chunk label embeddings. The cost is defined as follows:

J (3) = - d t log p(y (3) t = k|h (3) t ) +λ W ACD 2 + δ θ chunk -θ chunk 2 with p(y (3) t
= k|h

(3) t ) the probability of assigning the right label k to the word w t of the essay d. θ chunk is the set of parameters of the chunking layer right before the training of the ACD layer for the current epoch.

ACC layer

We denote θ ACC the set of parameters involved in the ACC layer, with θ ACC = (W ACC , b ACC , E P OS , E chunk , θ e ). W ACC and b ACC are respectively parameter matrices and bias of the ACC layer, including those of the chunking and POS tagging layers. The cost function is defined as follows:

J (4) = - d i log p(y (4) i = k|sh (4) i ) +λ W ACC 2 + δ θ chunk -θ chunk 2 with p(y (4) i = k|sh (4) i )
is the probability of assigning the right label k to the segment s i of the essay d.

Experiments and discussion

Hyperparameters and training corpora

Optimization

We trained the model alternating, for each epoch, the layers to be trained in the following order: POS tagging, chunking, ACD and ACC. We used Adam (Kingma and Ba, 2014) as learning algorithm, with β 1 = 0.9, β 2 = 0.999 and = 10 -8 . The learning rate was shared across all layers and was fixed to 10 -3 at the beginning of the training, and then multiplied by 0.75 every 10 epochs. In order to limit the gradient exploding problem, we used a gradient clipping strategy [START_REF] Pascanu | On the difficulty of training recurrent neural networks[END_REF]. We followed [START_REF] Hashimoto | A joint many-task model: Growing a neural network for multiple nlp tasks[END_REF] and used a clipping value of min(3.0, depth), where depth stands for the number of bi-GRU involved in the trained layer.

Parameters initialization

In order to smooth the backpropagation of the gradient during the training phase, we used random orthogonal matrices as initial weights for parameter matrices of every GRU, as suggested by [START_REF] Andrew M Saxe | Exact solutions to the nonlinear dynamics of learning in deep linear neural networks[END_REF]. The remaining parameter matrices were initialized with values drawn from a gaussian N (0, 2 n in ), with n in being the number of input neurons in the layer, as proposed by [START_REF] He | Delving deep into rectifiers: Surpassing human-level performance on imagenet classification[END_REF]. Bias vectors were initialized as zero vectors.

Vector dimensions used

We used 50 dimensional vectors for the words and labels embeddings. We used 100 dimensional vectors for the hidden states of every GRU in the model.

Regularization

Following [START_REF] Hashimoto | A joint many-task model: Growing a neural network for multiple nlp tasks[END_REF], we used λ = 10 -6 for the parameter matrices of every GRU and λ = 10 -5 for the remaining parameter matrices. The secondary regularization term rate δ was set to 10 -2 for each layer. We also used Dropout [START_REF] Srivastava | Dropout: a simple way to prevent neural networks from overfitting[END_REF] on every layer, with a probability to affect neurons set to 0.2.

POS tagging and chunking corpora

We used the corpora from the shared task CoNLL-2000 (Sang and[START_REF] Erik | Introduction to the conll-2000 shared task: Chunking[END_REF] and the associated labels to train the POS tagging and chunking layers.

ACD and ACC corpora

We used the Argument Annotated Essays (version 2) corpora released by [START_REF] Stab | Parsing argumentation structures in persuasive essays[END_REF] following the train/test split given to train the ACD and ACC layers.

Training termination criteria

For a mono-task model, a common practice is to stop the training right before overfitting. However, it is not clear to determine when to stop the training when dealing with multi-task models since it is possible that the model overfits only on a subset of the target tasks. Thus, we decided to stop the training when the model overfitted both on the ACD and the ACC tasks on a held-out validation set2 . The reported results are the best we obtained for each task on the test set, before overfitting on the validation set. Note that hyperparameters can be chosen so that the model overfits roughly at the same time on the ACD and ACC tasks.

Simple ACC

We denote Simple ACC the ACC task with the following modification: every segment corresponding to an argument component was treated as a single special token <EMPTY>. We hypothesize that this transformation will prevent the model from focusing on words inside argument components, but rather on its context, thus allowing a better generalization process.

Results and discussion

We report the obtained results on the test data for the tasks ACC, ACD and Simple ACC in Table 1.

The column "w/o POS & chunking" refers to the model version for which we omitted the training of the POS tagging and chunking layers, while the column "w/ POS & chunking" refers to the model version optimized for every tasks. As a baseline, we use the human performances3 and the results reported by [START_REF] Stab | Parsing argumentation structures in persuasive essays[END_REF], shown in Table 2.

General performance discussion

We obtained a macro f1-score of 0.8870 on ACD with the "w/ POS & chunking" model version, reaching human performance. This result was obtained without using handcrafted features, and is comparable to the one reported in [START_REF] Stab | Parsing argumentation structures in persuasive essays[END_REF]. Regarding the ACC task, we obtained a macro f1-score of 0.7980 with Simple ACC for the "w/ POS & chunking" version, representing 96.6 % of the performance obtained in [START_REF] Stab | Parsing argumentation structures in persuasive essays[END_REF] and 91.9% of the human performance.

Simple ACC assessment

We consider that the words composing argument components are not really relevant to determine if they are major claims, claims or premises. Hence, we hypothesize that focusing on those words will lead to model noise. Conversely, the context surrounding argument components should be a good indicator: sequences such as "we can receive the same conclusion that" seem to be strong indicators that the upcoming argument component is not a premise. This could explain the gap in performance obtained between ACC and Simple ACC, more particularly for the "w/ POS & chunking" version, with respective f1-scores of 0.7257 and 0.7980 (9.96% performance increase).

Multi-task framework assessment

Regarding the tasks ACD and Simple ACC, we obtained f1-scores of 0.5922 and 0.7670 for the "w/o POS & chunking" version and of 0.8870 and 0.7980 for the "w/ POS & chunking", representing respectively a 49.78% and a 4.1% performance gain. Those results show the benefits of using a multi-task framework and suggests that more subtasks could be added to the model.

Upcoming work and perspectives

The results we got are encouraging and could probably be improved, particularly by analyzing optimal hyperparameters in a deeper way. The performance difference between the "w/ POS & chunking" and "w/o POS & chunking" models tends to show that implementing more auxiliary tasks could be beneficial. One exploration way could be to insert a dependency parsing layer on top of the chunking layer, as done in [START_REF] Hashimoto | A joint many-task model: Growing a neural network for multiple nlp tasks[END_REF].

In order to implement a complete argument mining system, as introduced by [START_REF] Stab | Parsing argumentation structures in persuasive essays[END_REF], we plan to implement layers which enable to automatically generate argument graphs. Therefore, it is necessary to determine if a directed edge exists between each ordered pair of argument components, and also to label those edges either as support or as attack relationships.

Conclusion

This article introduced the use of a novel model based on a multi-task framework for automatically extracting and classifying argument components from raw texts. Interestingly, our results show that the use of advanced deep learning techniques enables competing with state-of-the-art systems that depend on handcrafted features. The variation of performance between the model exploiting auxiliary tasks (POS tagging and chunking) and a version skipping those tasks clearly promotes the added-value of a multi-task framework.

Figure 1 :

 1 Figure 1: Overview of the model architecture (Layerwise). POS, ACD and ACC are respectively acronyms for Part-Of-Speech, Argument Components Detection and Argument Components Classification.

Figure 2 :

 2 Figure 2: Excerpt from an essay of the corpus illustrating the notion of segments. Text regions underlined by a solid line are premises, those underlined by a dashed line are claims, and the bold regions are major claims.Segment numbers [S # ] were added as indications. The first segment is the region from the beginning of the text to the first premise. The second segment corresponds to the first premise. The third segment is the not underlined region between the first premise and the first claim, and so on.

Table 1 :

 1 Macros f1-scores obtained on the ACC, ACD and Simple ACC tasks.

	Task		w/o POS &	w/ POS &
			chunking	chunking
	ACD	0.5922	0.8870
	ACC	0.6950	0.7257
	Simple	0.7670	0.7980
	ACC
	Table 2: F1-scores reported on the ACD and ACC tasks
	by Stab and Gurevych (Stab and Gurevych, 2017) and
	human agents.
	Task	F1-score from (Stab	Human
	and Gurevych, 2017)	f1-score
	ACD		0.867	0.886
	ACC		0.826	0.868

We randomly sampled 10% essays from the training data to build the validation set.

Human performance corresponds the average performances reached by human agents, as presented in[START_REF] Stab | Parsing argumentation structures in persuasive essays[END_REF].