Arthur Laenger
email: arthur.laenger@univ-lemans.fr

Arnaud Martel

Michel Dabas

Joséphine Rouillard

Comparison of geophysical prospecting and geochemical prospecting at the Medieval and Modern Cistercian Abbey of Carnoët (Finistère, France)

Keywords: geochemical characterisation, geophysical prospection, soil analysis, EDXRF, GIS

Saint-Maurice Abbey in Carnoët (Finistère) underwent an in-depth archaeological appraisal during 2018-2019, involving several non-destructive technologies: the geophysical survey revealed the presence of expected structures, drawn on ancient plans, but also the presence of structures unknown up until now. A group of buildings on the edge of the pond in particular raised several questions. A geochemical survey was carried out there in order to try to characterise these buildings and observe the potential complementarity between geophysical and geochemical surveys. The results of the chemical analysis do not highlight the same level of detail of the structures as electrical resistivity, but these analyses seem to be able to clarify the geophysical diagnosis by discriminating signals of structures from echoes linked to the geological substrate.

1) Introduction

The chemical analysis of soils, through the dosing of phosphates, is an internationally used technique in archaeology since the 1930s [START_REF] Arrhenius | Die Bodenanalyse im Dienst der Archäologie[END_REF][START_REF] Arrhenius | Fosfathalten i skånska jordar[END_REF], and has been applied by French archaeologists since the 1950s [START_REF] Louis | Recherche des stations préhistoriques par la méthode des phosphates[END_REF]. Even today, it is systematically conducted to identify livestock-related structures, by establishing the link between phosphorus deposits and animal excrements [START_REF] Craddock | The Application of Phosphate Analysis to the Location and Interpretation of Archaeological Sites[END_REF]. However, the technique was extended to other chemical elements as early as the 1970s in Mexico [START_REF] Barba | Análisis de fosfatos en el piso de una casa habitada actualmente[END_REF] in order to characterise anthropogenic activities on a wider scale. The analytical methods described at the time may seem inadequate to the contemporary chemist, but they have been constantly developed and experimented with over the past decades [START_REF] Middleton | The Study of Archaeological Floors: Methodological Proposal for the Analysis of Anthropogenic Residues by Spot Tests, ICP-OES, and GC-MS[END_REF][START_REF] Dirix | Integrating Multi-element Geochemical and Magnetic Survey at Ancient Sagalassos (Southwest Turkey) : Anthropogenic Versus Natural Anomalies: Multi-element Geochemical and Magnetic Survey at Ancient Sagalassos[END_REF]. The latest improvement is the replacement of inductively coupled plasma analytical techniques (ICP-MS, ICP-AES, ICP-OES) with X-ray fluorescence spectrometry (XRF). Although this analytical technique only yields semi-quantitative results compared to ICP, these results are sufficient for the context of soil analysis for archaeological purposes based on variations in concentrations. Furthermore, comparative studies have shown a good correlation between results obtained by traditional techniques and XRF analysis [START_REF] Frahm | Chemical soil surveys at the Bremer Site (Dakota county, Minnesota, USA): Measuring phosphorous content of sediment by portable XRF and ICP-OES[END_REF]. In most cases, a field device is used, with minimal sample preparation [START_REF] Rippon | The use of soil analysis in the interpretation of an early historic landscape at Puxton in Somerset[END_REF][START_REF] Wilson | Multi-element soil analysis: An assessment of its potential as an aid to archaeological interpretation[END_REF][START_REF] Wilkins | Identifying 18th Century Hidden Landscapes at Stratford Hall Plantation Using Portable X-ray Fluorescence Phosphorus Readings on Plowzone Samples[END_REF][START_REF] Hayes | Parameters in the use of pXRF for archaeological site prospection: A case study at the Reaume Fort Site, Central Minnesota[END_REF][START_REF] Hafez | Geochemical survey of soil samples from the archaeological site Dromolaxia-Vyzakia (Cyprus), by means of micro-XRF and statistical approaches[END_REF][START_REF] Smejda | Ancient settlement activities as important sources of nutrients (P, K, S, Zn and Cu) in Eastern Mediterranean ecosystems -The case of biblical Tel Burna, Israel[END_REF][START_REF] Cannell | Delineating an Unmarked Graveyard by High-Resolution GPR and pXRF Prospection: The Medieval Church Site of Furulund in Norway[END_REF], Gutiérrez-Rodriguez et al. 2019[START_REF] Save | Large-scale geochemical survey by pXRF spectrometry of archaeological settlements and features : New perspectives on the method[END_REF]. One of the advantages of XRF is that it is easier to carry out analyses, as sample preparation is limited to drying and grinding instead of the acid digestion required for ICP. Another advantage of XRF is that all but a few chemical elements can be studied in a single analysis. However, above all, XRF presents huge prospecting potential as the broad spectrum of scanned elements can highlight significant differences in the concentration of elements not yet considered interpretable, paving the way for the identification of new markers of anthropogenic activity. In addition, since all of these data are recorded for each studied site, it is possible to return to sites retrospectively, once a marker has been clearly identified at another site.

As concentrations of chemical elements in soils are intricately linked to the geological environment, it is not so much a question of establishing target concentration values to distinguish an activity, but rather of identifying distribution anomalies in soils. This can be accomplished by establishing variations in elemental concentrations, which are available from semi-quantitative data. The aim of the study is to investigate the possibilities of combining geophysical prospecting, well known and often used by archaeologists, with geochemical prospecting [START_REF] Tabbagh | Soil resistivity: A non-invasive tool to map soil structure horizonation[END_REF][START_REF] Wiseman | Remote sensing in archaeology[END_REF][START_REF] Gaffney | DETECTING TRENDS IN THE PREDICTION OF THE BURIED PAST: A REVIEW OF GEOPHYSICAL TECHNIQUES IN ARCHAEOLOGY[END_REF][START_REF] Deiana | New Perspectives on Geophysics for Archaeology: A Special Issue[END_REF]. Although experiments have already been carried out outside France [START_REF] Persson | Integrated geophysical-geochemical methods for archaeological prospecting[END_REF][START_REF] Campana | Integration of ground remote sensing surveys and archaeological excavation to characterize the medieval mound (Scarlino, Tuscany-Italy)[END_REF][START_REF] Nolan | Geochemical and geophysical prospecting at three multicomponent sites in the Southwestern Lake Erie Basin: A pilot study[END_REF][START_REF] Cardarelli | Geophysical and geochemical techniques to assess the origin of rising damp of a Roman building (Ostia Antica archaeological site)[END_REF][START_REF] Conger | Archaeological reconnaissance through multi-method geophysical and geochemical survey at two Iroquoian village sites, southern Ontario, Canada[END_REF], Cuenca-Garcia 2019), to our knowledge this is the first application of this procedure on French soil. The Cistercian abbey of Saint-Maurice de Carnoët is a test site where both techniques were implemented in joint operations.

2) Context of the study site Saint-Maurice de Carnoët Abbey is located to the east of the commune of Clohars-Carnoët in Finistère. The geological context of the abbey is characteristic of the Morbihan; metamorphic complexes of the South Armorican Variscan domain, composed of Moëlan orthogneiss, Ordovician orthogneiss and Augen orthogneiss with locally migmatitic biotite. Near the Laïta River and the water bodies, we also observe the Nerly group, from the Pouldu unit, composed of often bedded micaceous leucocratic paragneiss [START_REF] Chauris | Les anciennes carrières de la région lorientaise (Morbihan)[END_REF]. It is one of thirteen Breton Cistercian abbeys located on a wooded hill near the bank of the Laïta River. The Ster Nadred stream was transformed into a pond, south of the abbey. The monastic complex was founded in 1170 and remained in the possession of the order until 1789, when it was sold as national property. Over time, the small rocky hill on which the abbey was built was transformed into terraces in order to meet the needs of the monastic community. It subsequently passed from hand to hand, was occupied by the German army during the Second World War, and was finally bought by the Conservatoire du Littoral (National Institution in charge of land policy to safeguard the coastal area) in 1991. The remains of the abbey are sparse, but the institution has begun restoration work and the orangery, the farmhouse and the abbey dwelling, the barn and chapter hall have now been restored. A joint geophysical and geochemical survey was undertaken as part of a general study of the site carried out in 2018 under the direction of Joséphine Rouillard [START_REF] Rouillard | Saint-Maurice de Carnoët (Finistère, France), une abbaye cistercienne entre terre et mer. Cister, Tomo III -Espiritualidade[END_REF]. The aim of this study is to assess the available documents, and carry out a technical preparation, prior to an archaeological excavation. The documentary assessment carried out by Joséphine Rouillard reveals that only rare and incomplete medieval and modern written sources exist for the abbey. Saint-Maurice is a modestsized abbey, with a small number of monks. It was not specialised and practised polyculture. The cultural and political influence of the abbey was very limited, as it only held a subordinate rank in the hierarchy of Breton Cistercian abbeys [START_REF] Dufief | Les cisterciens en Bretagne : XIIe-XIIIe siècles[END_REF]. The archaeological challenge is to study the conformity of the spatial organisation of the abbey of Saint-Maurice de Carnoët with the theoretical plan of the Cistercian order. It also intends to explore the underground hydraulic system, another speciality of the Cistercian order. Before beginning the excavation operation, the preliminary study can only be non-intrusive, as the site is classified as both a protected natural area and a historical monument. Geophysical prospection is particularly well suited to address these issues prior to excavation without causing any disturbance to the site. It was first used in May 2018 when it highlighted the layout of the abbey and revealed the internal hydraulic network (water supply and drainage). In addition, signals appeared in areas not covered by the old plans of the abbey. These areas were subsequently prospected during the complementary geochemical study, conducted in October 2019 and discussed in this article. In the end, the planned excavation operation never took place.

The aim was to check whether geochemical prospecting can provide additional and useful information to the archaeologist over and above that obtained by geophysical prospecting. In particular, the question is whether geochemical prospecting can be used to characterise archaeological structures. The rare known old plan of the abbey, dating from the eighteenth century (Rouillard et al. 2019, fig. 5 p. 243, fig. 6 p. 244) does not show a workshop for the maintenance of tools or a small forge. The mention of Cistercians and metallurgy immediately brings to mind large abbeys specialised in iron production [START_REF] Benoit | Moines & métallurgie dans la France médiévale[END_REF], but smaller abbeys were not necessarily dependent on the latter for their supply of materials. Smaller abbeys, such as Saint-Maurice, which were more focused on agriculture and animal husbandry, also needed to maintain tools. This would have been carried out in small workshops, as observed in other Cistercian abbeys [START_REF] Blary | Le Domaine de Chaalis, XIIe-XIVe siècles : Approches archéologiques des établissements agricoles et industriels d'une abbaye cistercienne[END_REF][START_REF] Astill | The Changing Monastic Cloister: Excavations in the South Range of Bordesley Abbey[END_REF][START_REF] Rouzeau | L'activité métallurgique à l'abbaye de Morimond (Haute-Marne) : Nouvel éclairage de la fouille à partir de l'analyse archéomagnétique de deux foyers[END_REF][START_REF] Blary | Abbaye de Preuilly[END_REF]). One hypothesis is that such a building might be present in the area studied in this article. The results illustrate the complementarity of these two types of prospecting.

3) Materials and methods 3.1) Sampling The sampling area covers 900 square metres on the edge of the western part of the abbey pond (Fig. 1). This area contains the features revealed by the geophysical survey (Fig. 2). Samples were taken from a quadrangular grid of two metres on each side, for a total of 256 samples. As the area is subject to a non-destructive archaeological survey permit, samples were taken using a gouge auger in the first sedimentary levels. It was not possible to excavate a test pit to check the stratigraphy. The first ten centimetres of soil, corresponding to the plant horizon, were removed with a hand hoe, and then samples were taken with the auger, on average from a depth of 15-20 cm in relation to the surface. It seems obvious that archaeological levels cannot be sampled with this sampling technique, as they lie at greater depths, but this does not contraindicate the implementation of a geochemical survey for archaeological purposes. In fact, the work of Cuenca-Garcia has shown that most of the concentrations of chemical elements interpreted in archaeology tend to be distributed vertically (Cuenca-Garcia 2013). It is thus possible to perceive deposits present deeper in the first levels. However, this effect entails a significant bias: the signal perceived on a surface point results from the addition of all the deposits of chemical elements beneath it, with no a priori possibility of discriminating them. However, this bias can be compensated for by focusing prospecting studies on structures that were abandoned after use and not subsequently reoccupied. This is the case for the hypothetical complex of buildings at the edge of the pond studied here.

3.2) Geophysical analysis

Geophysical surveying is a non-destructive subsurface mapping method often used in archaeology. It detects the location of potential archaeological remains, as well as their shape and orientation. This information is usually used to determine the location of archaeological excavations. The present campaign was carried out by the Géocarta company, which applied the Automatic Resistivity Profiler mapping method (ARP) [START_REF] Dabas | Theory and practice of the new fast electrical imaging system ARP[END_REF]. The principle of this technique is based on the measurement of the capacity of the soil to oppose the circulation of an electric current. The ARP system replaces the electrodes with spiked wheel-shaped dipoles placed on four axles. The first axle carries the dipole that injects the electric current into the soil. The next three axles carry the dipoles that measure the electrical potential difference. The spacing between each dipole electrode increases on each axle, respectively 0.5 m, 1 m and 2 m, so it is possible to investigate the soil at different depths. The current used is 5 mA. The recording of resistivity variations is almost continuous, with a measurement taken every 10 cm. The position of the device is recorded by a Trimble differential GPS with decimetric accuracy. It is thus possible to prospect a large area in a short period of time. The entire acquisition process at Saint-Maurice, extending over an area of 1.25 hectares, was carried out in a single day. In this way, it is possible to distinguish between resistive anomalies, where the electric current flows with difficulty, and conductive anomaly, where conversely, the electric current flows easily. The interpretation of areas with resistance or conductivity standing out from the background is then carried out by the Géocarta company.

3.3) Instrument and methods for sample preparation, XRF, pH and, thermogravimetric analysis (TGA).

3.3.1) Sample preparation Samples were oven-dried at 105°C for twelve hours. They were then ground using a Retsch RM100 mortar grinder made of sintered corundum. Once the sample is placed in the mortar, it is ground by pressure and friction, with a scraper in the device bringing the sample back to the pestle. The particle size of the powder obtained after grinding is less than 100 µm. The advantages of grinding for the study are twofold. The first is to homogenise the sample, the second is to obtain a sufficiently fine granulometry to ensure a good and reproducible surface profile for performing the best XRF measurements. Indeed, this technique is dependent on the surface condition and the density of the sample. The use of 100 µm powders achieves satisfactory accuracy and measurement repeatability.

3.3.2) XRF analysis, instrument and methods The instrument used for XRF measurements is a benchtop Epsilon 4 from Malvern Panalytical, more sensitive for the analysis of light elements such as P and S than portable XRF. The device is equipped with a silver anode X-ray tube with maximum power of 10 W, 50 kV voltage and a 2 mA current. Signal detection is via an SDD10 HR detector with a resolution of 135 eV at 5.9 keV. The spectrometer can perform elemental analysis from F to Am (K lines can be detected from F to Sb, and for elements from Te to Am, the L lines are used for quantifications and identification). During an analysis routine, each sample is bombarded directly and through the instrument's primary filters, namely 300 µm and 500 µm copper, 50 µm and 200 µm aluminium, 7 µm titanium and 100 µm silver. Using these filters makes it possible to concentrate the analysis on one part of the spectrum, limiting masking effects. In addition, the use of a helium stream is conducive to observing light elements with greater sensitivity. Measurements are performed using the Omnian method, the manufacturer's standardless semi-quantitative analysis software that automatically scans six conditions with different voltage, filter and current. The Omnian method was calibrated on delivery of the spectrometer and the gain is adjusted automatically with a Cu/Al alloy copper plate every twelve hours on Cu K,. The energy is calibrated on Al K and Cu K,. The measurements of the light elements are carried out under a helium flow according to the Omnian method and the sample spinner is activated. The measurements are taken in sample holders with an internal diameter of 30 mm, fitted with 6 µm mylar films in the form of loose powder. The Omnian method has been adjusted by the introduction of a tag in Omnian calibration from a soil standard (Light Sandy Soil 1, Certified reference material, BCR-142R). The processing parameters used for our samples are as follows: Loose, Powder, Film correction: Mylar 6µm, Drift correction, Compound list: Geology, Normalization required to 100%, Compton Intensity. The compound list Geology displays the most abundant elements as oxides (P: P2O5, S: SO3, Al: Al2O3, Si: SiO2, Na: Na2O, K: K2O, Mg: MgO, Ca: CaO, Fe: Fe2O3). One of the main advantages of XRF is to provide concentrations of most chemical elements directly on solid samples. However, EDXRF is unable to quantify the elements below F. This drawback is addressed by the Omnian software, which issues an evaluation of the latter to calculate the content of the analysed chemical elements. In order to reduce this randomness and make the results more reliable, by bringing the semi-quantitative results even closer to the quantitative value, Omnian can integrate the measurement of some elements into the processing parameters. Carbon dioxide (CO2), organic matter (CHON) and water (H2O) concentrations were then measured by thermogravimetric analysis (TGA) on a set of 12 samples spread over the sampling area (see supporting information). The mean values from these samples were then integrated to the Omnian processing parameters. Although an EDXRF semi-quantitative method is not as accurate in absolute techniques as traditional analysis techniques such as ICP, the results obtained display good repeatability and provide the most important data for this study, i.e., concentration variations depending on sampling coordinates. In order to focus our study on the variations, data were normalized between 0 and 1, 0 standing for the lowest value and 1 standing for the highest value.

3.3.3) TGA analysis, instrument and methods

As previously mentioned, in order to obtain the closest values as possible to the actual elemental contents of the samples, the contents of water (H2O), organic matter (CHON) and carbon dioxide (CO2) made up of light elements (C, H, N and O) were measured by thermogravimetric analysis (TGA) on 12 representative samples. The samples were exposed to the following temperature program using a Mettler Toledo brand TGA2 thermogravimetric analyser: gradient from 30°C to 150°C at 10K/min, followed by a 15 min stage at 150 ° C, next a new gradient from 150°C to 800°C at 20K/min, then a new gradient from 800°C for the decomposition of carbonates, followed by a 10 min stage at 1000°C. These measures are close to loss-on-ignition measurements. At 150°C, water evaporates, then from 150°C to 800°C, organic matter pyrolyzes and decomposes, while above 800°C, the carbonates decompose. In this way, the amount of each compound (residual water, organic matter or CO2 present in the samples) can be obtained by determining the respective mass gaps. An average of the values was then established, then these averages were integrated into the Omnian processing method (see supporting information).

3.3.4) pH analysis, instrument and methods

Finally, pH measurements were carried out in accordance with ISO 10390:2005(E) using a Metrohm pH Lab 827 pH meter, equipped with a 6.0228.010 probe.

3.4) Analysis of geochemical data

Not all the elements of the periodic table are represented. We only illustrate the elements present in sufficient quantities to exceed the quantitation limit of the instrument. In addition, we made a selection among the quantified elements, based on the presence of significant variations in concentrations. Variations in concentrations are shown according to the normalisation presented in 3.3.2. Values in ppm are given as an indication. Semi-quantitative analysis does not yield exact quantitative values, but it provides a very consistent order of magnitude.

Data from the analyses are integrated with archaeological data using a geographic information system (GIS), ArcMap from ArcGis version 10.7.1. This software caters for the management of spatial data, among others. In order to facilitate the reading of analysis results, we performed a spatial interpolation of data, transforming discrete data into continuous data. The most relevant interpolation method for our dataset is inverse distance weighting (IDW). This method is particularly effective when data are dense and evenly distributed in space. By superimposing the maps obtained on the archaeological data, such as the orthophotographs obtained by photogrammetry or the plans, it becomes possible to propose interpretations.

4) Results

The complete results of the geophysical prospecting appear in a previous general publication (Rouillard et al. 2019 fig. 23 p. 256). A group of conductive and resistance irregularities, located west of the cloister, particularly attracted our attention (Fig. 3). As no building was reported at this location in ancient documentation, we decided to carry out chemical prospecting on the highlighted anomalies, in order to evaluate the benefit of combining the two prospecting techniques.

Fig. 3 -High-pass filtered resistivity data for the ARP channel and interpretation

Apparent resistivity values span over a wide range of values due to the proximity of the bedrock: from 130 to 1300 for channel1 (0 to 0.5 m); 120 to 1400 (0 to 1 m) and 100 to 2100 for channel 3(0 to 1.7 m). In order to better observe the results, we show filtered maps, i.e., with the regional component removed. For convenience, we separated the study area into two zones, labelled zone 1 and zone 2. Zone 1 shows a set of coherent and clear conductivity anomalies. The resistive anomalies form a rather regular rectangular polygon of 42 m by 17 m. They are surrounded by conductive anomalies which seem to go around them. They are probably located between 50 and 100 cm below ground level. The impact on the third track in zone 1 is minimal. In zone 2, only resistive anomalies appear. They are interlocked and superimposed, making interpretation more difficult. The anomalies in this zone are visible on all three tracks, placing them 50 to 170 cm underground.

With XRF, it is possible to analyse a large number of chemical elements, but not all of them are relevant for archaeological interpretation. For example, silicon (Si) and aluminium (Al), the components of aluminium silicates which are widespread in the earth, are particularly prevalent in soils. It is generally not interesting to represent them. We show them in the article, alongside iron (Fe) concentrations, as the representations of these three elements show very similar distribution patterns (Fig. 4). Given the initial assumption of a workshop containing a small forge, we represented commonly used metals by ancient societies, namely tin (Sn), zinc (Zn) and copper (Cu), in addition to iron (Fig. 5). These metals can be used to make alloys but can also be present as impurities in ores. The pH, calcium (Ca), phosphorus (P) and sulphur (S) show a highly polarised distribution (Fig. 6 and Fig. 7). All the high values of these elements are found in zone 1 defined above, i.e., the lower part of the study area.

The other elements with significant variations but which will not be interpreted further in this article are available in the supporting informations section.

5) Interpretation

The results of the geophysical survey in the study area show numerous anomalies (Fig. 3). In zone 1, the resistive anomalies outline clear contours suggesting the presence of a building. The latter is surrounded by conductive anomalies, which may correspond to an old path or to a pipe system, either for water supply or drainage, operating with this building. Zone 2 is more complex to interpret. The intertwined signals could be the result of the superposition of several ancient occupations, but also of anomalies in the geological substratum. We should bear in mind that the monks developed their environment by creating the pond to the south of the study area, but also by cutting terraces into the rocky hillside. Local rocks were also used for the construction of the collective buildings of the abbey [START_REF] Rouillard | Saint-Maurice de Carnoët (Finistère, France), une abbaye cistercienne entre terre et mer. Cister, Tomo III -Espiritualidade[END_REF]) and the signals in zone 2 may possibly be related to this work.

The highly polarised distribution of pH values immediately caught our attention (Fig. 6a). It seems to highlight a difference between two blocks of signals perceived by geophysical prospecting, distinguishing the northern and southern parts, respectively zone 2 and zone 1. This high pH partly overlaps with the calcium distribution zones (Fig. 6b), which is not surprising. Indeed, the presence of this element in soils increases the pH value, and it is commonly used in agriculture to reduce soil acidity. However, this is not the case here. Although the soil is particularly acidic, as the site is located in Brittany, a region of France with a granite base, it is not a question of agriculture. The presence of calcium is rather related to the presence of the structure revealed by the geophysical survey. It is specific to the architectural characteristics of the structure. Indeed, the use of limestone blocks and lime mortar leaves this type of signature in the soil. Here, given the construction materials used on the other buildings on the site, it is more likely that mortar was used than limestone blocks, which were reserved for the decorative elements of the chapter hall. The asymmetric distribution between zone 1 and zone 2 suggests that only zone 1 contains anthropic remains, whereas the anomalies in zone 2 may be more related to geological anomalies. It would be easy to confirm these hypotheses by the archaeological excavation of these zones, as initially planned. Unfortunately, the cancellation of this operation leaves these questions pending.

We can, however, answer the archaeologist's initial hypothesis of a tool maintenance workshop with a small forge. Such a structure should reveal high concentrations of the metals used in the tools. In addition, metal waste could have been detected. The observation of iron distribution (Fig. 4a) shows fairly evenly distributed concentrations. We find the same type of distribution pattern as for aluminium (Fig. 4b) and silica (Fig. 4c). It therefore seems logical to attribute the measured iron concentrations to the sedimentary matrix rather than to anthropogenic activity. Iron is also naturally present in all soils, but in cases of metallurgical activity, it is still possible to show the enrichment of the soil with iron. We were able to observe this distribution at other archaeological sites. In addition, we analysed the other metals commonly used in metalworking in former societies, namely tin, zinc and copper. Tin also shows a very homogeneous distribution, suggesting that it is also a constituent of the sedimentary matrix (Fig 5a). Zinc (Fig 5b) and copper (Fig 5c) show specific peaks, which partly coincide in zone 1. The presence of these elements is undoubtedly due to human activity, but it is difficult to affirm that this corresponds to intense ancient metallurgical activity. On the other hand, light metallurgical activity might not have left visible traces. Indeed, it is not impossible that these metal concentrations correspond to waste fires made by the former operators of the site. According to information obtained from the testimony of the site's custodian for the Conservatoire du Littoral, these practices are contemporary. Indeed, he observed the remains of fireplaces demonstrating such practices when the site was acquired by the Conservatoire, although he was unable to pinpoint their precise location. The initially planned excavation would have helped to clarify this point but it did not take place.

However, if the hypothesis of a workshop exclusively for the forge no longer seems relevant, the distribution of the two remaining chemical elements leads us to propose another interpretation for zone 1. Phosphorus also shows a highly polarised distribution, with high values in the south of the survey area, called zone 1 (Fig. 7b). Phosphorus is an element traditionally linked to farming areas in archaeology. In reality, phosphorus seems to be a more general marker of the presence of organic matter, and not exclusively faecal. Indeed, phosphorus can be found in burials, storage areas or even food preparation areas. Nevertheless, on this site, the hypothesis of a building for housing livestock appears to be the most likely. If we observe sulphur concentrations (Fig. 7a), we notice once again that all the high concentrations are in zone 1. The enrichment of soils in this element is generally due to the presence of livestock, as a result of the accumulation of urine and external envelopes (hair, skin, nails, etc.) [START_REF] Shand | Sheep urine affects soil solution nutrient composition and roots: Differences between field and sward box soils and the effects of synthetic and natural sheep urine[END_REF][START_REF] Kear | Soil Extractable Organic Sulfur and Sulfate Increases Under Dairy Cow Feces and Urine Patches: The Consequences for Soil Testing[END_REF]. Thus, while phosphorus alone cannot be used to accurately identify a livestock structure due to its ubiquitous nature in organic matter in general, the combination of phosphorus and sulphur is more relevant. Here, in zone 1, both phosphorus and sulphur are present. The abbey's rare archives indicate the presence of horses and cattle in the inventory prior to the sale of the abbey during the Revolution [START_REF] Rouillard | Saint-Maurice de Carnoët (Finistère, France), une abbaye cistercienne entre terre et mer. Cister, Tomo III -Espiritualidade[END_REF]). It is obviously not possible to affirm that these animals lived in this building, but this does confirm that there were livestock on the site. Finally, if we look at the interpretation of geophysical anomalies, we observe a resistant anomaly in the shape of an E in the eastern part of zone 1 which could possibly be stalls to accommodate the animals.

Although it is not possible to propose a date, it should be noted that the building does not figure in eighteenth century texts and plans, nor in the revolutionary inventory [START_REF] Rouillard | Saint-Maurice de Carnoët (Finistère, France), une abbaye cistercienne entre terre et mer. Cister, Tomo III -Espiritualidade[END_REF]. This shows that the memory of this building had disappeared for a very long time by the time those documents were created. It is therefore possible that this building disappeared at quite an early stage, perhaps in the late medieval, early modern period. Buildings with the same characteristicsrectangular building, multiple activities -and similar dating have been observed in the abbeys of Morimond [START_REF] Rouzeau | L'activité métallurgique à l'abbaye de Morimond (Haute-Marne) : Nouvel éclairage de la fouille à partir de l'analyse archéomagnétique de deux foyers[END_REF] and Preuilly [START_REF] Blary | Abbaye de Preuilly[END_REF]. They were characterised during the excavations of these abbeys as multi-purpose buildings. Although we could not observe any traces of metallurgy at Saint-Maurice, perhaps as a result of very limited activity, we can nonetheless suggest that the probable building in zone 1 was associated with the corpus of these polyvalent buildings.

6) Conclusion and discussion

The results of this study highlight the importance of prospecting prior to more invasive archaeological operations. Geophysical prospecting revealed the buried remains of unknown structures, which do not appear in any of the ancient documents of Saint-Maurice Abbey. Geochemical prospecting clarifies the results of geophysical prospecting. It was deployed on the structures revealed by the first survey, and seems to distinguish anomalies due to archaeological remains from those due to irregularities in the geological substratum in zone 2. It is possible that the signals in this zone are evidence of the anthropization of the hill by the monks. But the chemical analysis of the sediments can go further. The initial working hypothesis of a tool maintenance workshop with a small forge is challenged by the absence of characteristic chemical soil pollution. It is possible, however, that light and very occasional activity may have left few chemical traces of this activity in the soil. On the other hand, other chemical elements found in the building in zone 1 seem to indicate the presence of livestock. The geochemical survey thus enables us to propose a new interpretative hypothesis. The probable building identified by the combination of prospecting data may be reminiscent of the multipurpose buildings found on other Cistercian abbeys [START_REF] Rouzeau | L'activité métallurgique à l'abbaye de Morimond (Haute-Marne) : Nouvel éclairage de la fouille à partir de l'analyse archéomagnétique de deux foyers[END_REF][START_REF] Blary | Abbaye de Preuilly[END_REF]). It should be noted, however, that these latter buildings show marked traces of metallurgical activity despite their status as small workshop forges, whereas such traces seem to be absent at Saint-Maurice. Unfortunately, it will not be possible to verify this interpretation as no excavation work will be conducted at the site, contrary to initial plans. The two prospecting techniques discussed in this study cannot be placed in competition with each other and are best used together. The advantage of geophysical prospecting is that it is easily applicable over large areas and effectively locates the geometry of buried structures, but at the same time it cannot always precisely identify what it reveals. Geochemical prospecting necessitates a large number of samples, and is thus difficult to apply to large areas with a fine grid. In addition, it records all the activities that have taken place in the area, without being able to distinguish between the different phases of occupation. Moreover, the processing and analysis of samples takes longer than for the production of soil resistivity images. However, in the same way as for geophysical prospecting, the study of chemical pollution deposited in soils helps to locate archaeological structures but also to characterise them. The combination of the two techniques provides the archaeologist with a sharp tool for approaching an archaeological site and planning future operations there. In the present case, it was possible to overcome the indeterminacy of the geophysical survey results with the help of chemical soil analysis. It was also possible to distinguish a likely archaeological structure from a probable geological signal. The prior location of remains using the soil resistivity technique enabled us to set up a narrow sampling grid, and geochemistry was used to enhance interpretations. This technique is not intended to replace a traditional excavation operation as it does not provide sufficient information used alone, at least not in terms of dating. Nonetheless, soil prospecting and its initial interpretations can help the archaeologist to choose the best zone for operations and to anticipate the specialised techniques to be implemented. The chemical study of soils yields interesting prospecting results, especially if it is backed up by geophysics, so that it can target the probable remains. The geochemical analysis procedure is relatively long, but the time required could be reduced if all or part of the analyses were conducted directly in situ. In any case, it seems more interesting to carry out sampling and chemical analysis directly on archaeological levels, as the signal is stronger there, and the different areas can be characterised more precisely, partly compensating for the bias of superimposed activities. It should be noted that this does not in any way conflict with traditional stratigraphic excavations. As samples are taken once the study of the level has been completed, the geochemical study focuses on sediments that would otherwise have been discarded on spoil heaps. However, information obtained by the chemical analysis of the soils must still be cross-referenced with the archaeological results in order to propose the most relevant interpretations.

Supporting information

In this section, we present the elemental concentration maps not interpreted in this article. We also make available data acquired by XRF.

Fig. 1 -

 1 Fig. 1 -Location of the studied zone (Satellite image: Geoportail)

Fig. 4 -

 4 Fig. 4 -Interpolation of concentrations or iron (a), aluminium (b) and silicon (c)

Fig. 5 -

 5 Fig. 5 -Interpolation of concentrations of tin (a), zinc (b) and copper (c)

Fig. 6 -

 6 Fig. 6 -Interpolation of pH values (a) and concentrations of calcium (b)

Acknowledgements

We thank Gwendoline Bertein, intern at the CReAAH-Le Mans, who participated in the study of samples from the Saint-Maurice abbey. We also thank Merlin Muzellec, doctoral student at the CReAAH studying Cistercian abbeys in Brittany, for topographic data on the Saint-Maurice Abbey.

Conflict of interest

We have no conflict of interest to declare.


Tatiana Anikina
email: tatiana.anikina@dfki.denatalia.skachkova@dfki.de

Natalia Skachkova

Joseph Renner
email: joseph.renner@inria.frpriyansh.trivedi@inria.fr

Priyansh Trivedi

Anaphora Resolution in Dialogue: System Description (CODI-CRAC 2022 Shared Task)

) for bridging resolution given gold mentions and anaphors.

Introduction

In this paper we present our systems submitted for the CODI-CRAC 2022 Shared Task (CCST) on Anaphora, Bridging, and Discourse Deixis in Dialogue2 [START_REF] Yu | The CODI-CRAC 2022 shared task on anaphora, bridging, and discourse deixis in dialogue[END_REF]. The task is a follow-up to the one held last year and described in [START_REF] Khosla | The CODI-CRAC 2021 shared task on anaphora, bridging, and discourse deixis in dialogue[END_REF]. As its name suggests, besides identity anaphora this shared task tries to cover other, less-studied, anaphoric phenomena, and offers new multi-genre data that combines several types of annotations in Universal Anaphora3 format.

Main focus of the shared task is on dialogue. Dialogue data offers new challenges, like grammatically incorrect utterances, disfluencies, more deictic references, speaker grounding and long-distance conversation structure [START_REF] Khosla | The CODI-CRAC 2021 shared task on anaphora, bridging, and discourse deixis in dialogue[END_REF]. While coreference resolution in text has been very actively studied in the recent years, it is much less researched in dialogue, especially such forms as bridging, or discourse deixis. Descriptions of early systems implemented for the resolution of 'standard' and discourse deictic pronouns in dialogue can be found, e.g., in [START_REF] Byron | Resolving pronominal reference to abstract entities[END_REF], [START_REF] Strube | A machine learning approach to pronoun resolution in spoken dialogue[END_REF], [START_REF] Müller | Fully automatic resolution of'it[END_REF]. More approaches (not implemented), together with some useful findings are presented, e.g., in [START_REF] Rocha | Coreference resolution in dialogues in English and Portuguese[END_REF], [START_REF] Eckert | Dialogue acts, synchronizing units, and anaphora resolution[END_REF], and [START_REF] Navarretta | Resolving individual and abstract anaphora in texts and dialogues[END_REF].

CCST 2021 stirred new interest in coreference resolution in dialogue. The majority of systems submitted for it represent various modifications of either the higher-order coreference resolution model (coref-hoi) by [START_REF] Xu | Revealing the myth of higher-order inference in coreference resolution[END_REF], or one of the earlier models by [START_REF] Joshi | BERT for coreference resolution: Baselines and analysis[END_REF] and [START_REF] Lee | Higherorder coreference resolution with coarse-to-fine inference[END_REF]. These models were originally trained on the text data, and are span-based -each span gets associated with a score, and anaphor-antecedent pairs are established based on the pairwise scores. Designed for identity anaphora resolution, these models were also adapted for bridging and discourse deixis resolution. Examples of span-based models submitted for CCST 2021 include systems by [START_REF] Kobayashi | Neural anaphora resolution in dialogue[END_REF], [START_REF] Renner | An end-to-end approach for full bridging resolution[END_REF], [START_REF] Xu | Adapted end-to-end coreference resolution system for anaphoric identities in dialogues[END_REF]. Other participants presented different approaches. Thus, [START_REF] Kim | The pipeline model for resolution of anaphoric reference and resolution of entity reference[END_REF] perform identity anaphora and bridging resolution using pointer networks. [START_REF] Anikina | Anaphora resolution in dialogue: Description of the DFKI-TalkingRobots system for the CODI-CRAC 2021 shared-task[END_REF] cast anaphora resolution as a clustering problem, and discourse deixis resolution -as a Siamese Net based scoring function.

Inspired by the success of the span-based coreference resolution models, we submit three independent systems for CCST 2022. Our system for identity anaphora resolution uses both the Workspace Coreference System by [START_REF] Anikina | Anaphora resolution in dialogue: Description of the DFKI-TalkingRobots system for the CODI-CRAC 2021 shared-task[END_REF] and the coref-hoi model as described in Section 2. The model for discourse deixis extends coref-hoi with shallow linguistic features and aims at resolving three types of potential anaphors. It is described in Section 3. The model for bridging resolution is a modification of the system by [START_REF] Renner | An end-to-end approach for full bridging resolution[END_REF]. The approach is explained in Section 4.

Anaphora Resolution

For the anaphora resolution track we trained and combined the outputs of the Workspace Coreference System (WCS) and the coref-hoi system (see Table 1). While working on the shared task we realized that a combination of different models performs better than a single model and we explored various settings to find an optimal solution.

Data

For training of the WCS system we used the datasets recommended by the shared task organizers. These include the ARRAU corpus (Gnome,Trains_91,Trains_93,RST_DTreeBank,Pear_stories), AMI, Switchboard, Light and Persuasion data. We used the development sets of AMI, Light and Persuasion for the internal evaluation and comparison of different configurations. We trained our system using the gold mention spans to avoid any mistakes introduced by the mention extraction module and used SpaCy [START_REF] Honnibal | SpaCy: Industrial-strength Natural Language Processing in Python[END_REF] for mention extraction during the test phase.

For training of the coref-hoi system, we utilized the CoNLL 2012 English Shared Task dataset [START_REF] Pradhan | CoNLL-2012 shared task: Modeling multilingual unrestricted coreference in OntoNotes[END_REF] to supplement the datasets listed in the previous paragraph. Note that this CoNLL 2012 data does not include singleton coreference clusters, but the current dialogue shared task datasets do.

Model architecture

WCS Our model is based on the implementation described in [START_REF] Anikina | Anaphora resolution in dialogue: Description of the DFKI-TalkingRobots system for the CODI-CRAC 2021 shared-task[END_REF]. It creates coreference clusters incrementally and compares each new mention to the clusters that are available in the workspace. The general flow of the model is presented in Figure 1. The model uses separate layers to encode each pair of mentions where one mention represents a workspace cluster and another mention is a candidate that is being clustered. WCS passes the concatenated embeddings of the candidate mention and the cluster member through several feed-forward neural layers with the input and output dimensions shown in Table 2.

The network also encodes the absolute position of each mention within the document and generates a separate embedding for each speaker. The model combines this information with different word embeddings. For each mention it extracts the head and encodes it with a combination of contextual BERT embeddings [START_REF] Devlin | BERT: Pre-training of deep bidirectional transformers for language understanding[END_REF] (bert-base-cased) together with GloVe [START_REF] Pennington | Glove: Global vectors for word representation[END_REF] and Numberbatch [START_REF] Speer | Concept-Net 5.5: An Open Multilingual Graph of General Knowledge[END_REF] embeddings. Unlike [START_REF] Anikina | Anaphora resolution in dialogue: Description of the DFKI-TalkingRobots system for the CODI-CRAC 2021 shared-task[END_REF] we do not generate a new random embedding for each unknown word, but take an average embedding based on all words in the GloVe and Numberbatch vocabularies. This gave us slightly better results in the pilot experiments.

In order to represent the spans we take an average of all individual word embeddings based on BERT and GloVe correspondingly. We also experimented with SpanBERT embeddings but did not observe any improvements. E.g., when we replaced our span embeddings with SpanBERT and left the rest of the system unchanged we achieved 66.68% CoNLL F1 score when training and evaluating on the Light dataset. After replacing SpanBERT with standard BERT and simply averaging span embeddings we achieved 67.23% CoNLL F1 score on the same data. Removing GloVe embeddings and leaving only BERT, SpanBERT and Numberbatch or training on more data samples also did not help. We suspect that since SpanBERT embeddings have high dimensionality (representing span start, span end and span head) they dominate mention representation in WCS and allow some vague semantic matches. E.g., with SpanBERT we generated clusters that included mentions like 'war' and 'peace' or 'the jamaica tourist board' and 'jamaican'. Training for more epochs or adjusting hyperparameters might help to improve clustering but the configurations that we tested have not shown an improvement.

The WCS system combines three cross-entropy losses that are added in each forward pass. The main clustering loss compares the true cluster probabilities vs. the computed ones. The true probabilities are computed with respect to the mentions that are currently in the workspace. For each mention

Track

Resolution of anaphoric identities

Setting

Predicted mentions Baseline WCS [START_REF] Anikina | Anaphora resolution in dialogue: Description of the DFKI-TalkingRobots system for the CODI-CRAC 2021 shared-task[END_REF] and coref-hoi model [START_REF] Xu | Revealing the myth of higher-order inference in coreference resolution[END_REF] Approach 1) Extract all nominal phrases with SpaCy 2) Run WCS trained on the Shared Task dialogue data 3) Run coref-hoi with cluster merging trained on the CoNLL 2012 data 4) Combine the outputs of WCS and coref-hoi the probability of being in that cluster is defined as the ratio of mentions that are in the same gold cluster and the current cluster over all mentions in that cluster. The coherence loss computes the difference between the gold cluster assignments and the system assignments. Basically, we create two matrices that align mentions to each other and check the overlap between these matrices in the gold annotations vs. the generated outputs (the matrix has ones if two mentions belong to the same cluster and zeros otherwise). The referring loss is used for the referring expression classification which is a binary classification task. It is needed since not all mention spans extracted by SpaCy are valid referring expressions.

After computing clustering probabilities for each mention and clusters in the workspace we apply softmax and select the cluster with the highest probability. After that the workspace is updated and some clusters are moved to the history if they have not been updated for more than 100 steps. After the initial clustering we apply some post-processing as explained in [START_REF] Anikina | Anaphora resolution in dialogue: Description of the DFKI-TalkingRobots system for the CODI-CRAC 2021 shared-task[END_REF].

We have also evaluated WCS in combination with a Crosslingual Coreference System (CCS) based on AllenNLP and SpaCy pipelines4 . We noticed that WCS performs quite well on identifying singletons and clusters with personal pronouns but has more difficulties with other nominal phrases. Hence, in one of the experiments we combined the output of the CCS model trained on OntoNotes that uses MiniLM [START_REF] Wang | MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers[END_REF] for mention representation with the outputs of WCS trained on the shared task data. Among the clusters generated with CCS we selected only those that do not contain any personal pronouns and from WCS we took singletons and clusters with pronouns.

We also experimented with some compatibility checks. E.g., we checked whether the first and second mentions in the cluster have the same number and we removed the first mention from the WCS cluster if the embedding similarity between the first pronoun and the first noun in that cluster was too low (compared to the cosine similarity between the first pronoun and other mentions in the cluster). E.g., mentions such as 'a presenter' and 'I' could belong to the same cluster with pronouns but mentions like 'table' and 'I' should not. We run WCS with these modifications on the shared task test set and report our results in Table 3. The final version that was submitted to the leaderboard combines WCS outputs with the coref-hoi system as described in the next section.

Coref-HOI Combination

We trained a "cluster merging" variant of the coref-hoi model. As this model was developed using the data from 2012 CoNLL dataset, which does not include singleton clusters, the model does not output singleton predictions off the shelf (one could potentially use the scores for the "dummy" antecedent as a proxy, but this could be noisy as the model is not trained to differentiate singleton clusters from simple mentions 4. Looking at these scores, we found that coref-hoi struggled with singleton clusters (as expected), as the CoNLL F1 score of these predictions was much higher after removing the singletons from the annotations. WCS, on the other hand, seemed to do better on singletons than non-singletons, as evidenced by the higher scores on annotations that contain singletons vs. those without. As a result, we combined the strengths of the two systems by simply adding the singletons predictions of WCS to the cluster predictions of coref-hoi. This resulted in the highest test set scores (as shown in Table 3).

Training WCS

The WCS system was trained for 5 epochs on Nvidia GeForce RTX 2080. We use teacher forcing for the coreference clusters with a ratio of 30%. The learning rate is set to 1e-4 and the dropout rate is 0.3. We use Adam as optimizer. It took about 26 hours to train the whole system on the complete training set.

Coref-HOI The coref-hoi system was trained for 24 epochs on a Nvidia Quadro RTX 6000. We use a pretrained SpanBERT Large model to initialize the base language model. We use a learning rate of 1e-5 for the base model and 3e-4 for the fine tuning layers. We follow all other hyperparameters found in the train_spanbert_large_ml0_cm_fn1000 training configuration of the coref-hoi system. Training took about 24 hours.

Results and discussion

Our results on the internal development set as well as on the official test set are reported in Tables 3 and4. Based on the final cluster assignments we can recognize 4 common types of mistakes made by WCS: partial word overlaps (e.g., 'mute button' and 'volume button'), embedded mentions (e.g., 'a power supply which we get' and 'we'), wrong span boundaries (e.g., 'ok good knight') and confusing candidates that have similar surface forms but different meanings (e.g., 'the minutes of uh this meeting' and 'forty minutes'). Some of these mistakes were probably caused by the over-reliance of WCS on the head embeddings. Interestingly, when using SpanBERT instead of GloVe and standard BERT for span encoding we observed that many generated clusters contain mentions with spurious connections (e.g., 'the spirits of our people' and 'such dark superstitions' or 'the executive' and 'the company').

Judging from the scores on the development set reported in Table 4, WCS shows better performance than coref-hoi when the evaluation is done on all clusters including singletons. However, when singletons are excluded coref-hoi outperforms WCS and this was the main motivation to combine the outputs of both models. We also evaluated the span extraction performance of WCS vs. the combined system using the gold mention span annotations provided by the shared task organizers. We found that WCS had consistently higher recall but lower precision on mention span detection compared to the combined model. E.g., on the AMI dataset WCS achieved precision 82% and recall 68% whereas the combined model achieved precision 84% and recall 63%. Similar results were observed on the other two datasets that we tested (Light and Persuasion).

Looking at the mistakes of the combined model we found that some mentions have incorrect spans, e.g., 'half' and 'hour' are annotated as two separate mentions in 'see you in half and hour'. Sometimes the annotated spans are longer than the gold ones, e.g., 'close tabs on you' instead of 'close tabs' or 'Of course , good Monk' instead of 'good Monk'. This can also result in incorrect clustering such as in case of putting 'this realm' and 'this realm, stories, population' in the same cluster. The combined model also struggles with the cases like 'some' and 'they' in the following example: 'Some don't give the money out like they are suppose to. Did you heard that they now do every payment taken from people transparent?' Both mentions were assigned to the same coreference chain although 'some' should refer to the people who give the money and 'they' to those who receive it. Despite some problems with the mention span detection the combined model shows overall better clustering performance compared to vanilla WCS.

Experimenting with various combinations of the coreference systems we found that combining the strengths of different systems helps to improve the results. In the future we plan to investigate whether adding coreference signal from the pretrained models also helps boost the performance and reduce training time for systems like WCS.

For the current submission we combined the model outputs based on some simple heuristics but it would be interesting to see whether this process could be also learned by a model. Training a new model from scratch or even fine-tuning it on a new dataset might be sub-optimal or even not feasible in some cases. E.g., when we deal with dialogues instead of narrative texts or if the annotation schemes differ significantly. In such cases we believe that using a smart coreference editor that combines and checks outputs of different systems and applies some constraints or filters would be beneficial and we would like to work on such project in the future.

Discourse Deixis Resolution

CCST 2022 offers three different tracks for discourse deixis resolution. First track (Eval-DD Pred) assumes finding antecedents for discourse deixis anaphors predicted by models given unannotated data. The second one (Eval-DD Gold M) aims at identification of discourse deixis anaphors among all types of annotated anaphors and non-referential mentions, and their subsequent resolution. The goal of the last track (Eval-DD Gold A) is to find antecedents for already annotated discourse deixis anaphors. Our team participated in all three tracks.

The core of our approach relies on the corefhoi model, because it was successfully adopted for CCST 2021 discourse deixis track by [START_REF] Kobayashi | Neural anaphora resolution in dialogue[END_REF]. Their model was able to achieve the CoNLL F1 score of 35.4% -52.1% depending on the dataset and shared task track, and ranked first for discourse deixis [START_REF] Kobayashi | Neural anaphora resolution in dialogue[END_REF]. The summary of our system can be found in Table 5.

Data

We use training and development data presented in Section 2.1. Coref-hoi splits input data into segments of a set length to limit the number of mention candidates. Given a segment, all possible spans/potential mentions are created. Next, this 'pool' of mentions is used to form valid anaphorantecedent pairs. In contrast to that, we only consider the occurrences of 'this', 'that', 'it' and 'which' as potential anaphors and treat all other spans in the segment as antecedent candidates. These four markables were chosen based on our observation that they often occur as discourse deixis anaphors in our training data: they make about 72.3% of all annotated discourse deictic anaphors5 . Similar statistical findings (however, for other dialogue corpora) were reported, e.g., by [START_REF] Webber | Discourse deixis and discourse processing[END_REF], [START_REF] Müller | Fully automatic resolution of'it[END_REF], [START_REF] Kolhatkar | Anaphora With Non-nominal Antecedents in Computational Linguistics: a Survey[END_REF]. Besides being discourse deictic, the markables in focus can also be non-referential (e.g., 'it' in expletive constructions, 'that' as a relative pronoun), or anaphoric (e.g., 'this' as a determiner in a noun phrase).

Because we focus only on certain anaphor candidates, we build segments in a slightly different way than coref-hoi does. Instead of splitting the input into non-overlapping chunks of approximately the same length, we go through the input data word by word until any of our anaphors occurs, and then create a segment. Our segment typically includes all (sub)tokens up to the current sentence end to the right of the anaphor, as well as one or more sentences to the left of it. We limit the segment's length by 256 (sub)tokens. Thus, given the same input, we build more segments than coref-hoi does, our segments are mostly overlapping, and each one contains only one anaphor candidate.

In total we build 9,827 segments/examples from training data, of which 44% contain non-referential 'this', 'that', 'it' and 'which', 41.2% -anaphoric, and only 14.8% -discourse deictic ones. To make our training data balanced, we perform undersam-

Track

Resolution of discourse deixis Setting

Predicted mentions / Gold mentions / Gold anaphors Baseline

The coref-hoi model adopted for discourse deixis by [START_REF] Kobayashi | Neural anaphora resolution in dialogue[END_REF] Approach 1) Consider all mentions of this, that, it and which potential anaphors 2) Consider all spans in the given segment potential antecedents 3) Represent both anaphor and antecedent candidates as embeddings with additional shallow linguistic features 4) Calculate pairwise anaphor-antecedent scores similar to coref-hoi and choose the antecedent based on the largest score 5) Use anaphor-antecedent pair representation to classify the anaphor type and discard non-discourse deictic anaphors

Train data

ARRAU corpus (Gnome, Trains_91, Trains_93, RST_DTreeBank, Pear_stories), AMI, Switchboard, Light and Persuasion Dev data AMI, Light, Persuasion (dev splits) pling and decrease the number of examples from the first two classes. We end up having 1,454 training samples of each anaphor class. For the sake of simplicity, undersampling is done blindly, i.e. we do not take into consideration how the instances our three classes are distributed given each of the four markables.

Model architecture

We perform discourse deixis resolution using a multi-task learning approach -besides finding the antecedents, we also need to identify the types of potential anaphors (discourse deictic, anaphoric or non-referential). Type classification is performed after the antecedent (if any) is found. It is also important to emphasize that we try to resolve any potential anaphor regardless of its type. Thus, our model also learns to resolve 'standard' coreference as a by-product. To our knowledge, our model is the first one doing that.

To perform the resolution, coref-hoi first associates each span (represented as an embedding) with a score indicating how likely this span is a valid mention (anaphor or antecedent). To speed up the training process, certain number of spans with the low scores get pruned. Next, the model learns to find the most probable antecedent for each anaphor based on their pairwise scores.

We modify their approach as follows. First, as we know exactly which span our anaphor x is, and it is the same for all antecedent candidates y, we do not score anaphors or calculate pairwise mention scores. An antecedent score s m (y) is produced by a feedforward neural network FFNN m taking as input a vector representation of span y, like in coref-hoi. Second, as shown in Table 6, anaphors k x = p x , ρ(x) and antecedents q y = g y , ψ(y) are composed differently. Main representations p x and g y are concatenated with shallow linguistic features ρ(x) and ψ(y) to help our model better differentiate between types of anaphors and antecedent candidates. Our approach to mention representation and motivation behind it are explained in more detail in Section 3.3. Third, we do not prune any unlikely antecedents due to the fact that each segment only contains one anaphor, which often has only one antecedent (if mention is anaphoric, there can be more). If we apply pruning, this only antecedent is very likely to be lost at the early stages of training.

s(x, y) = s m (y) + s f (x, y) + s s (x, y) s m (y) = FFNN m (q y ) q y = g y , ψ(y) k x = p x , ρ(x) s f (x, y) = k x • q y s s (x, y) = FFNN c (k x , q y , ϕ(x, y)) (1)
As shown in Equation group 1, the final anaphorantecedent score is the sum of three components:

(1) anaphor score s m (y); (2) fast score s f (x, y), which is an inner product of vectors k x and q y representing anaphor and antecedent, respectively; (3) slow score s s (x, y), which is an output of a different network FFNN c taking as input an anaphorantecedent pair and pairwise features ϕ(x, y). Two of pairwise features are borrowed from the corefhoi model. They are distance feature, showing how many sentences/utterances lie between the starting tokens of two mentions, and similarity feature, which is simply a result of am element-wise multiplication of anaphor and antecedent candidate vectors. Finally, we add a token distance feature that shows how many (sub)tokens lie between the starting tokens of the two mentions. This feature is used to help our model learn that in case both anaphor and its antecedent are parts of the same sentence, their starting tokens cannot be close to each other.

The largest s(x, y) score is used to predict the best antecedent candidate. The antecedent gets concatenated with the anaphor and is used as input for an anaphor type classifier, which is a multilayer perceptron (MLP) network consisting of two linear layers with a ReLU activation function inbetween. Similar to coref-hoi, to account for the case of non-referential 'anaphors', a dummy zero score is always prepended to the row of s(x, y) scores.

Mention representation

Potential anaphors and antecedents have different representations. While the main part of an antecedent candidate embedding g y is constructed similar to coref-hoi, the main part of an anaphor embedding p x is a concatenation of the embedding of the token itself, embedding of the parent token and local context embedding, which includes eight (sub)tokens to the left and right of the anaphor.

Our decision to include the last two embeddings was motivated by the following observations. Depending on the mention type, mentions' parents have to certain extent different distributions, e.g., discourse deictic mentions more often have forms of the verb 'to be' as parents than mentions of other two types (see Table 11 in Appendix A). Moreover, in our data about 60% of anaphor candidates have verbal parents. And certain verbs (e.g., 'assume', 'say') are only compatible with discourse deixis [START_REF] Eckert | Dialogue acts, synchronizing units, and anaphora resolution[END_REF]. We use SpaCy to identify tokens' parents, and SpanBERT Large encoder to acquire tokens' embeddings. The usage of context helps capture various useful patterns that may be characteristic of discourse deixis or identity anaphora. These patterns may include, e.g., adjective-copula constructions. Subjects of such constructions with adjectives applicable to abstract entities (e.g., 'correct', 'true') usually refer to discourse entities [START_REF] Eckert | Dialogue acts, synchronizing units, and anaphora resolution[END_REF]. Other examples are certain types of complement constructions (like 'that is why/because/what/how'), 'doobject' expressions, which also may point at verbal antecedents [START_REF] Müller | Fully automatic resolution of'it[END_REF]. The inclusion of context may also be useful for capturing any tokens that point at abstract/concrete character of reference. The size of the context window was chosen intuitively, we did not conduct any separate experiments for finding the optimal window size, but may do it in the future.

Additional linguistic features used to represent anaphors ρ(x) and antecedent candidates ψ(y) are also different. Again, we use SpaCy to extract part of speech (POS) and dependency edge (DEP) tags for tokens in segments, and Berkeley Neural Parser [START_REF] Kitaev | Multilingual constituency parsing with self-attention and pretraining[END_REF] to get syntactic constituents (nominal, verbal, or other). We use POS and DEP tags for anaphors. According to our statistical findings (see Table 12 in AppendixA), there are some differences in distributions of (POS, DEP) combination depending on the mention type. E.g., the (PRON, nsubj) combination is especially frequent in case of discourse deictic anaphors, while (DET, det) is not. Our antecedent candidates encompass four additional features, of which only span width is borrowed from coref-hoi. Other features include span type (verbal, noun, other), POS and DEP tags of the last token. The span type feature was introduced based on the observation that discourse deictic anaphors mostly have verbal phrases or sentences as antecedents, and 'standard' anaphors -noun phrases. The other two features are meant to help identify discourse entities, which often encompass the whole sentence and thus end with a punctuation mark. Note that none of our shallow linguistic features is decisive. Moreover, both SpaCy and Berkeley Neural Parser may not function properly on dialogue data. Still, our experiments on the toy dataset (consisting of a single light_train 2022 file) show that without all these features the model is only able to achieve 29.41% CoNLL F1 score on the light_dev 2022 data. Adding features helps increase this score up to 36.44%.

All linguistic features described in this section are represented as trainable embeddings of length 100.

Training

To train our model we kept the hyperparameters reported by coref-hoi, namely BERT-and taskspecific learning rates (1e-5 and 3e-4, respectively), optimizers (AdamW and Adam), schedulers and dropout rate of 0.3. The number of training epochs was set to 24, but we had to stop training after 17 epochs. Currently the model is computationally inefficient (it is able to process only a single training example at a time), so we did not have enough time to complete the training.

The model was trained using a combination of several loss functions: (i) marginal log-likelihood of possibly correct antecedents; (ii) anaphor type loss checking how well the model distinguishes between discourse deixis, identity and non-referential anaphors; (iii) label loss that punishes the model if it tends to reject all antecedent candidates while having a referential anaphor; (iv) constituent type loss checking how well the model can differentiate between valid (verbal and nominal) and invalid (various fragments) antecedents. The addition of label loss is motivated by the fact that at early stages of training our model always tends to reject all antecedents by assigning negative scores to them. Constituent type loss is inspired by the mention loss in coref-hoi. The idea is that the model should assign larger scores to valid constituents. This loss is used with a coefficient λ = 0.02 to account for a big number of constituents and prevent it from dominating over all other losses.

Results and discussion

We used the same model for all three discourse deixis tracks. Table 7 illustrates the scores achieved by our model on the official test sets. Because the model is designed to resolve only four potential antecedents, there is no big difference in scores between the (Pred) and (Gold M) tracks. The scores for the latter are even slightly worse, as the model has to deal with numerous anaphor candidates it has not seen before. The best scores are reached for the (Gold A) track. It should be noted that here the model tries to resolve all annotated anaphors, not only the four target ones. Still, we tend to attribute the increase in performance not to a wider coverage of anaphors, but to the fact that the model does not have to classify the anaphor types. Table 8 shows the CoNLL F1 scores achieved by our system and the winning model on the official test data 2022 for the Eval-DD (Pred) track. Our model ranks second for all the datasets with a score difference ranging from 0.27 to 7.55 points. To compare our model with the baseline model by [START_REF] Kobayashi | Neural anaphora resolution in dialogue[END_REF], we also evaluate it on the test partitions of Light, AMI and Persuasion datasets without gold annotations released for the CCST 2021. We see that our approach beats the baseline on all the datasets.

To see the limitations of our model and have a better understanding of what it can/cannot learn, we additionally evaluate it on the test partitions of Light, AMI and Persuasion datasets from CCST 2021 containing gold annotations. Our analysis (see Table 13 in Appendix A) shows that the model struggles with the anaphor type identification: out of 292 true discourse deictic 'this', 'that', 'it' and 'which' only 212 (72.6%) are classified as having the same type, 62 (21.25%) -as anaphoric, and 18 (6.16%) as non-referential ones. Interestingly, only one of all misclassified anaphors is linked to the correctly predicted antecedent. Also, all anaphors incorrectly classified as non-referential get associated with empty spans. At the same time the model successfully finds antecedents for 144 (67.92%) out of 212 correctly identified discourse deictic anaphors. It looks like anaphor type is important for the model to be able to perform resolution.

Looking at Table 13, we can conclude that our model also has difficulties finding split antecedents: 41 anaphors (14.04%) out of 292 refer to them, but our model only finds 7. In general, the model demonstrates a tendency to choose discourse deixis antecedents consisting of single sentences. We hypothesize that it happens for the following reasons. First, there are not enough training examples with split antecedents. Second, our model lacks mechanisms to capture relations between split antecedents making them a coherent piece relative to a discourse deictic anaphor.

The following points should also be emphasized. So far we have not evaluated the performance of our model separately for each of the four anaphor candidates. We have not analyzed the ability of our model to resolve identity anaphora. However, such analysis would be useful, so we plan on conducting it in the future. Also, using a lot of features slows down the training process. Therefore we are planning to perform experiments testing different combinations of features and various feature embeddings sizes. Additional experiments on how the usage of features influences the model trained on all available training data are also necessary. Furthermore, an investigation of the quality of the constituent types, POS and DEP tags would be beneficial, considering that we use SpaCy and Berkeley Neural Parser on dialogue data, while they were trained on text corpora.

Bridging Resolution

In this section we introduce our submission for the resolution of bridging references. We submitted to the Eval-Br (Gold A) track, in which gold mentions and anaphors are given. This reduces the problem to the selection of antecedent (from gold mention candidates) for each given anaphor.

Data

In addition to the shared task dialogue datasets of AMI (851 bridging instances across 7 documents), Switchboard (603 instances, 11 documents), Light (381 instances, 20 documents), and Persuasion (245 instances, 21 documents), we also utilize the bridging anaphora resolution datasets of BASHI [START_REF] Rösiger | BASHI: A corpus of Wall Street Journal articles annotated with bridging links[END_REF] and ISNotes [START_REF] Markert | Collective classification for fine-grained information status[END_REF] to train our models. BASHI is a corpus of 50 Wall Street Journal articles, containing 57,709 tokens and 410 bridging pair annotations. ISNotes is a corpus of Wall Street Journal articles as well, containing 663 bridging pair annotations. The inclusion of these supplementary datasets was important, as the shared task datasets are relatively small, and the model architecture is fairly complex and expressive, making it easy to overfit.

Model architecture

Our approach is based on "independent" variant of the higher-order coreference architecture introduced in [START_REF] Joshi | BERT for coreference resolution: Baselines and analysis[END_REF]. We make a number of modifications to the architecture and training procedure (an overview of the original model/architecture can be found in [START_REF] Joshi | BERT for coreference resolution: Baselines and analysis[END_REF] and the system it is built on, introduced in [START_REF] Lee | Higherorder coreference resolution with coarse-to-fine inference[END_REF]. Note that the coref-hoi system proposed alternatives to the original higher-order system presented in [START_REF] Joshi | BERT for coreference resolution: Baselines and analysis[END_REF], but these alternatives (such as the cluster merging model variant) are not relevant for our system, as we are not finding clusters of coreferent mentions.

Our modifications follow that of the bridging resolution system introduced in [START_REF] Renner | An end-to-end approach for full bridging resolution[END_REF]. The first modification is a result of the gold anaphors being given: since we do not need to detect anaphors from the text, we can pass one anaphor at a time into the model (together with the document text and gold mentions) instead of passing the whole document at once and detecting and resolving potential anaphors. While this means potentially processing each document multiple times if there are multiple bridging anaphors in the document, this is done to decrease memory requirements significantly, as the pairwise scoring function is run for just one anaphor with its candidates, instead of many anaphors with all of their candidates. This decrease in memory usage allows for changes to the architecture that make it simpler and more accurate (see next paragraph). Also, in practice, the bridging datasets are relatively small, so this extra processing of the same document results in a negligible decrease in computational efficiency.

The architecture modifications are made possible by the decrease in memory usage allowed from having the mentions given and processing one anaphor at a time. Recall that in the original architecture by [START_REF] Lee | Higherorder coreference resolution with coarse-to-fine inference[END_REF], they use a "two stage beam search" when detecting mentions and finding coreferent pairs: first, they prune potential mentions based on a span scoring function, then they prune antecedents for each span based on a "fast" bilinear scorer (the "coarse" part of the coarse-to-fine scorer), before sending the remaining spans and their list of antecedent candidates to the more computation-and memory-heavy "fine" scorer. This beam search was proposed to allow the system to scale better to longer documents. By having the gold mentions, we can remove the "fast" span scorer from the original model, as we no longer need to enumerate all possible spans. Also, since the pairwise memory restraints are reduced by passing just one anaphor into the model at a time, we can remove the "coarse" pairwise scorer and skip directly to the "fine" scorer. We make these changes in order to use the more expressive "fine" scorer directly on all pairs, without having to filter possible mentions and antecedents based on the less expressive 'fast" span scorer and "coarse" pairwise scorer.

After these modifications, the model architecture is as follows: pass entire document through the base contextual language model, obtain span representations for the gold mentions and anaphors, compute antecedents via the higher-order mechanism introduced in [START_REF] Lee | Higherorder coreference resolution with coarse-to-fine inference[END_REF]. Also, this allows the use of cross entropy loss over all possible antecedents for each anaphor, instead of the original marginal log-likelihood, leading to a more direct optimization of the pairwise scorer.

We use bert-base-uncased as our base language model.

We use this instead of bert-large-uncased because the resulting embedding is of smaller dimensionality, leading to less parameters in our token attention and span pair scoring layers. We experimented with the Span-BERT variant as well, but this led to slightly lower scores in preliminary experiments.

Training

We trained the system for 5 epochs on a single Tesla P100 GPU. The learning rate was set to 3e-3 and we used Adam optimizer. We froze the base BERT model to prevent overfitting as the dataset is relatively small even with the supplementary data, set the dropout to 0.3 in the fine tuning layers, and used a higher-order depth of 2. It took about 1 hour to complete training.

Results and discussion

The submission Entity-F1 scores are shown in Table 10. Overall, we report scores slightly higher than reported in [START_REF] Renner | An end-to-end approach for full bridging resolution[END_REF] for bridging resolution, with scores on the Persuasion dataset being significantly higher than on the other three datasets. This setting allows for a more direct evaluation of the span embedding and pairwise scoring mechanisms from [START_REF] Joshi | BERT for coreference resolution: Baselines and analysis[END_REF] and [START_REF] Lee | Higherorder coreference resolution with coarse-to-fine inference[END_REF], as we can remove steps in the fine tuning architecture that are only needed to manage memory usage. These results show the effectiveness of the span embedding and pairwise score on span comparisons tasks such as gold mention/anaphor bridging resolution.

Conclusion and Outlook

In this paper we presented our systems for identity anaphora, bridging and discourse deixis resolution.

Our system for the identity anaphora resolution combines the outputs of WCS and the coref-hoi system trained with "cluster merging". It ranked second in the shared task competition. When experimenting with WCS we tested different settings and tried replacing and adding different embeddings for mention representations (e.g., SpanBERT). However, the configuration reported in [START_REF] Anikina | Anaphora resolution in dialogue: Description of the DFKI-TalkingRobots system for the CODI-CRAC 2021 shared-task[END_REF] turned out to work best on our development set. We also tested a combination of WCS trained on the shared task data and CCS trained on OntoNotes as well as coref-hoi trained on a combination of dialogue and non-dialogue datasets. The analysis of the model outputs shows that WCS works reasonably well for detecting singletons and pronominal clusters but performs worse when clustering noun phrases. Hence, we combine the outputs of WCS and the coref-hoi model and achieve an average improvement of 7.95% CoNLL score over vanilla clustering with WCS.

In the future we would like to do a more fine-grained analysis of the combined model outputs and test if one could use automatic coreference annotations from other pre-trained models as a weak supervision signal for WCS. In particular, we are interested in evaluating this model on the domain adaptation task and in the low resource setting. We would also like to perform more experiments with coreference chain editing based on the outputs of several models.

The system for discourse deixis resolution ranked second for all three tracks of the shared task. It was able to reach the CoNLL F1 scores ranging from 35.91% to 62.79% depending on the track and dataset. Some of these scores are close to the scores achieved by the winning team.

The model is based on a novel idea that it is possible to combine the tasks of discourse deixis and anaphora resolution. It is our first attempt at implementing this idea, so there is much space for improvement and additional analysis. First, we plan on making our model computationally more efficient, namely, we are going to perform some experiments with adaptive span pruning and check the influence of linguistic features given a larger training set. Second, it is possible to expand the set of potential anaphors. Before doing that, we need to analyse the ability of our model to resolve identity anaphora. Depending on the results, we may use our discourse deixis resolution model to enhance the coreference resolution performed by the WCS model. Finally, the phenomenon of split antecedents requires more investigation, namely, how we can model coherence/relations between them.

The system for the resolution of gold bridging anaphors is based on a higher order coreference system adapted for the setting. While the gold mentions/anaphors setting is much simpler than full bridging (mention/anaphor detection and resolution), the results show how well the span embedding and pairwise scoring mechanisms from [START_REF] Joshi | BERT for coreference resolution: Baselines and analysis[END_REF] and [START_REF] Lee | Higherorder coreference resolution with coarse-to-fine inference[END_REF] work for bridging pairs.

A Appendix: Discourse Deixis

Here we present statistical findings used to pick out features to represent anaphor candidates. Table 11 shows the relative frequencies of parent tokens' lemmas for three types of 'anaphors': discourse deictic, anaphoric and non-referential. Table 13 presents an error analysis of our discourse deixis resolution model on the test Light, AMI and Persuasion data from CCST 2021. We analyze the antecedent predictions made by our model as follows. If they are not empty, all predicted antecedents are divided into split and not split, depending on a simple heuristics: if a predicted sequence of (sub)tokens (the very last token is always excluded) contains a dot, a question or an exclamation mark), it is considered to be split. Next, we check if the antecedents' borders are correct. Here, four cases are possible: (i) only the left border is wrong; (ii) only the right border is wrong; (iii) both borders are wrong; (iv) both borders are correct.

The table also shows the anaphor type predicted by the model for all 292 gold discourse deictic anaphors.

  Figure 1: Workspace Coreference System Overview

Table 1 :

 1 Anaphora resolution: approach summary

	Encoder	Input dim Hidden dim Output dim
	BERT head	2*768	900	600
	BERT span	2*768	900	600
	Numberbatch	2*300	600	300
	GloVe head	2*100	600	200
	GloVe span	2*100	600	200
	BERT masked LM	2*768	600	200
	Table 2: Separate encoders are used to represent men-
	tion pairs in WCS. Additionally, distance between the
	mentions, their positions in the document and corre-
	sponding speakers are encoded and added to the final
	representation.

Table 4 :

 4 Evaluation of WCS and coref-hoi on dev sets. NS (No Singletons) refers to annotations with singleton clusters removed. Scores presented are CoNLL F1 scores. Note that the scores are from an internal development set.

	Setting	Light Light NS AMI AMI NS Persuasion Persuasion NS ARRAU ARRAU NS
	WCS	65.39	61.48	43.33	35.85	61.23	56.55	45.02	32.93
	coref-hoi 59.84	76.89	43.30	54.70	60.60	81.00	48.32	66.97

Table 5 :

 5 Discourse deixis resolution: approach summary

Table 6 :

 6 Representations of anaphor and antecedent candidates, and pairwise features

Table 7 :

 7 CoNLL F1 scores on the official test sets

	Track	Light AMI Persuasion Swbd.
	Eval-DD (Pred)	36.82 50.09	47.04	n/a
	Eval-DD (Gold M) 35.91 47.13	48.24	n/a
	Eval-DD (Gold A) 44.95 56.54	62.79	n/a
	Data	2021		2022
	Our model Winner Our model Winner
	Light	48.04	42.7	36.82	37.09
	AMI	40.34	35.4	50.09	53.31
	Persuasion	56.68	39.6	47.04	54.59
	Swbd.	n/a	35.4	n/a	49.76

Table 8 :

 8 Model comparison: CoNLL F1 scores on official tests 2021 and 2022 for the Eval-DD (Pred) track

Table 9 :

 9 Bridging resolution: approach summary

Table 10 :

 10 Test set results for the bridging task (gold anaphors)

	Switchboard Light Persuasion AMI
	35.78	37.68	50.99	35.23

Table 11 :

 11 Table 12 illustrates the joint distribution of POS and DEP labels of possible anaphor candidates, also depending on their type. All numbers were extracted from the CCST 2021 training data, namely ARRAU, Light, AMI, Persuasion, and Switchboard. Distribution of anaphors' parents depending on the anaphors' types

				Anaphor's parent
				DD	ID	non-ref
			s	0.329 be	0.139 be	0.202
			be	0.235 s	0.117 s	0.078
			do	0.040 have 0.048 have 0.031
			about 0.037 do	0.038 like	0.022
		Lemma	sound 0.020 use like 0.020 ... ...	0.027 make 0.022 ...
			have	0.017
			...
			make 0.013
			...
			use	0.003
				Mention
			DD		ID	non-ref
	POS+DEP	(PRON, nsubj) 0.664 (PRON, nsubj) (PRON, dobj) 0.148 (PRON, dobj) 0.249 (SCONJ, mark) 0.173 0.46 (PRON, nsubj) 0.390 (PRON, pobj) 0.117 (DET, det) 0.139 (DET, det) 0.138 (DET, det) 0.03 (PRON, pobj) 0.074 (PRON, pobj) 0.110
		(PRON, mark) 0.013 (PRON, dep)	0.02 (PRON, dobj)	0.097

Table 12 :

 12 Distribution of anaphors' POS and dependency edges tags depending on the anaphors' types

Table 13 :

 13 Performance on the test partitions of AMI, Light & Persuasion datasets from CODI-CRAC 2021 Shared Task

Proceedings of the CODI-CRAC 2022 Shared Task on Anaphora, Bridging, and Discourse Deixis in Dialogue, pages 15-27 Gyeongju, Republic of Korea, October 17, 2022.

An average improvement over all 4 datasets is 7.95%.

https://codalab.lisn.upsaclay.fr/ competitions/614#learn_the_details

https://universalanaphora.github.io/ UniversalAnaphora/

https://pypi.org/project/ crosslingual-coreference/

We treat all discourse deictic markables with semantic type 'discourse old' as anaphors.

Acknowledgements

The authors are supported by the German Ministry of Education and Research (BMBF): T. Anikina in project CORA4NLP (grant Nr. 01IW20010); N. Skachkova, P. Trivedi, J. Renner in IMPRESS (grant Nr. 01IS20076).