Vincent Truffault

Gisèle Riqueau

Cécile Garchery

Hélène Gautier

Rebecca G Stevens
email: rebecca.stevens@inra.fr

Is monodehydroascorbate reductase activity in leaf tissue critical for the maintenance of yield in tomato?

Keywords: MDHA(R): monodehydroascorbate (reductase) DHA(R): dehydroascorbate carbon limitation, ascorbate redox, Solanum lycopersicum, genetic background, yield

enzymes that produce or remove the radical monodehydroascorbate, the semi-

oxidized form of ascorbate (ascorbate oxidase or peroxidase and monodehydroascorbate reductase respectively). Previous work in cherry tomato has revealed correlations between monodehydroascorbate reductase and ascorbate oxidase activity and fruit yield: decreased whole plant MDHAR activity decreases yield while decreased whole plant ascorbate oxidase activity increases yield under unfavourable environmental conditions. We aimed to investigate if similar effects on yield are obtained in a large-fruited variety of tomato, Moneymaker. Furthermore we wished to establish whether previously observed effects on yield in cherry tomato following changes in whole plant enzyme activity could be reproduced by reducing MDHAR activity in fruit only by using a fruit-specific promoter in cherry tomato (West Virginia 106). In Moneymaker, RNAi lines for monodehydroascorbate reductase did not show significant yield decrease compared to control lines when plants were grown under

Introduction

The redox status of plant cells influences plant growth and development through the network involving reactive oxygen species, antioxidants and hormones [START_REF] Bartoli | Interactions between hormone and redox signalling pathways in the control of growth and cross tolerance to stress[END_REF][START_REF] Considine | Redox regulation of plant development[END_REF][START_REF] Kocsy | Redox control of plant growth and development[END_REF]Schippers et al., 2016). As ascorbate is present in high concentrations in all cells, its redox status, which is maintained by the enzymes of the ascorbate-glutathione cycle, is important in controlling the redox state of the cell. Under optimal growth conditions, ascorbate and glutathione redox couples are maintained in a highly reduced state. The reduced form of ascorbate is oxidized, enzymatically or directly, into monodehydroascorbate (a radical; MDHA), with the loss of a single electron; dehydroascorbate (DHA) is then generated from the disproportionation of the radical form or the loss of a second electron from MDHA. Recycling of ascorbate is carried out by monodehydroascorbate reductase (MDHAR) which is an NADH-dependent enzyme and dehydroascorbate reductase (DHAR) which uses glutathione as an electron donor. Two DHAR genes have been mapped in tomato (Stevens et al., 2007) and MDHAR also belongs to multigene family as shown by the presence of three isoforms in tomato (Stevens et al., 2007) and six in Arabidopsis [START_REF] Lisenbee | Arabidopsis peroxisomes possess functionally redundant membrane and matrix isoforms of monodehydroascorbate reductase[END_REF]. The isoforms, localized in different organelles, can have different biological effects on global metabolism; the isoform targeted in this study, MDHAR3, is a cytosolic and peroxisomal enzyme (whereas MDHAR1 and MDHAR2 have been identified respectively in chloroplasts and in peroxisomes; [START_REF] Gest | Light-dependent regulation of ascorbate in tomato by a monodehydroascorbate reductase localized in peroxisomes and the cytosol[END_REF][START_REF] Haroldsen | Constitutively expressed DHAR and MDHAR influence fruit, but not foliar ascorbate levels in tomato[END_REF]). Dual targeting to both peroxisomes and cytosol has also been reported for glutathione reductase which is another ascorbate-glutathione cycle enzyme [START_REF] Kataya | Arabidopsis glutathione reductase 1 is dually targeted to peroxisomes and the cytosol[END_REF]). It appears that chloroplastic MDHAR activity correlates positively with ascorbate content [START_REF] Li | Overexpression of chloroplastic monodehydroascorbate reductase enhanced tolerance to temperature and methyl viologen-mediated oxidative stresses[END_REF], while MDHAR3 has shown negative correlations with ascorbate content [START_REF] Gest | Light-dependent regulation of ascorbate in tomato by a monodehydroascorbate reductase localized in peroxisomes and the cytosol[END_REF][START_REF] Haroldsen | Constitutively expressed DHAR and MDHAR influence fruit, but not foliar ascorbate levels in tomato[END_REF] in different genotypes.

Ascorbate and the activity of ascorbate recycling enzymes have been shown to be correlated to plant growth and yield. For example, in rice, over-expression of DHAR led to an improvement in terms of yield and biomass which was associated with an increase in photosynthetic activity, but also to better tolerance of environmental stress in the field [START_REF] Kim | Homologous expression of cytosolic dehydroascorbate reductase increases grain yield and biomass under paddy field conditions in transgenic rice (Oryza sativa L. japonica)[END_REF]. The authors suggested this was related to the co-activation of MDHAR, ascorbate peroxidase and glutathione reductase leading to higher ascorbate content and a less oxidized state of the ascorbate pool. High ascorbate content resulting from over-expression of the biosynthetic pathway genes also led to positive effects on growth and biomass in Arabidopsis [START_REF] Lisko | Elevating vitamin C content via overexpression of myo-inositol oxygenase and l-gulono-1,4-lactone oxidase in Arabidopsis leads to enhanced biomass and tolerance to abiotic stresses[END_REF]. It has also been shown that silencing of ascorbate oxidase activity improved yield in cherry tomato under unfavorable conditions [START_REF] Garchery | A diminution in ascorbate oxidase activity affects carbon allocation and improves yield in tomato under water deficit[END_REF] and the opposite effect was seen on reducing MDHAR3 activity, again in cherry tomato: yield and vegetative growth were decreased with more pronounced effects being seen under conditions of carbon limitation (Truffault et al., 2016). At a cellular level, ascorbate metabolism and growth (cell division and expansion) are often closely related: the reduced form of ascorbate seems to stimulate mitosis [START_REF] De Pinto | The redox state of the ascorbatedehydroascorbate pair as a specific sensor of cell division in tobacco BY-2 cells[END_REF]Liso et al., 1984) unlike DHA which is an inhibitor of cell cycle progression (Potters et al., 2000). It has also been shown that the radical MDHA stimulates cell expansion [START_REF] Gonzalez-Reyes | Nutrient Uptake Changes in Ascorbate Free Radical-Stimulated Onion Roots[END_REF]. Ascorbate oxidase activity also seems to stimulate cell expansion [START_REF] Kato | Expansion of transgenic tobacco protoplasts expressing pumpkin ascorbate oxidase is more rapid than that of wild-type protoplasts[END_REF] and the enzyme is suggested to regulate auxin levels at least in root meristems [START_REF] Kerk | Auxin metabolism in the root apical meristem[END_REF].

These studies point to pleiotropic effects of the ascorbate molecule and its oxidized forms on both whole plant and cellular physiology. We have been particularly interested in how the ascorbate redox state controls growth processes, in particular yield, under different conditions.

The genetic background is also a deciding factor in the establishment of yield as different genotypes have very different harvest indices and growth habits (determinate or

A C C E P T E D M A N U S C R I P T
Comment citer ce document : Truffault, V., Riqueau, G., Garchery, C., Gautier, H., Stevens, R. (2018). Is monodehydroascorbate reductase activity in leaf tissue critical for the maintenance of yield in tomato?. Journal of Plant Physiology, 222, 1-28. , DOI : 10.1016/j.jplph.2017.12.012 4 indeterminate). Domestication has increased the harvest index of many crops, including tomato (Tanksley, 2004), and in tomato has led to breeding of larger-fruited genotypes [START_REF] Cong | Natural alleles at a tomato fruit size quantitative trait locus differ by heterochronic regulatory mutations[END_REF][START_REF] Frary | fw2.2: a quantitative trait locus key to the evolution of tomato fruit size[END_REF] typically with harvest indices of up to 60-65% [START_REF] Ho | Partitioning of assimilates in fruiting tomato plants[END_REF]van der Ploeg et al., 2007). Cherry tomatoes represent an intermediate type of tomato between cultivated tomato (Solanum lycopersicum) and the closest wild ancestor (Solanum pimpinellifolium) and are probably the first domesticated form (Nesbitt and Tanksley, 2002;Ranc et al., 2008), their harvest index tends to be much lower compared to modern largefruited varieties, presumably because of increasing sink strength of fruit during domestication.

We have previously identified differences in yield in cherry tomato when enzymes controlling ascorbate recycling are manipulated [START_REF] Garchery | A diminution in ascorbate oxidase activity affects carbon allocation and improves yield in tomato under water deficit[END_REF]Truffault et al., 2016). The yield phenotypes appeared to be correlated to photosynthesis and sugar metabolism and particularly the environmental conditions. Our hypothesis was that carbon metabolism and/or translocation of sugars from leaves to fruits was affected in these plants. To test this hypothesis we developed two parallel experiments firstly we observed effects on yield in a large-fruited tomato where the harvest index (the ratio of total fruit yield to total plant biomass) is higher than in cherry tomato and therefore may be subject to different controls determining yield. For this the cultivated genotype Moneymaker was chosen. We studied the impact of modified ascorbate recycling in Moneymaker, under optimal growth conditions and conditions of carbon limitation, applied by leaf removal. Secondly we tested the hypothesis that sugar allocation was affected between leaves and fruit as previous results had been shown using plants with reduced whole-plant MDHAR activity. Thus, we investigated the modification of ascorbate recycling in fruits only as compared to the whole plant by using a fruit-specific promoter to control MDHAR silencing. We show that MDHAR activity does not significantly affect yield in Moneymaker. Also a reduction in fruit MDHAR activity does not affect yield in contrast to a reduction in whole plant MDHAR activity.

Materials and methods

Plant material

Moneymaker

Solanum lycopersicum L. cv Moneymaker cotyledons were transformed using the MDHAR RNAi construct as previously described [START_REF] Gest | Light-dependent regulation of ascorbate in tomato by a monodehydroascorbate reductase localized in peroxisomes and the cytosol[END_REF] et al., (2013). In the present study, the independent line under-expressing MDHAR3 is mds5. This line, conjointly with 2 others independent lines under-expressing MDHAR3 (mds3 and mds42), have been previously characterized in terms of MDHAR activity and yield (Truffault et al., 2016). The RNAi fragment was also cloned into a Gateway TM compatible vector, pK8GWIWG-PPC2-B4, where the 35S promoter was replaced with a fruit specific promoter from the phosphoenolpyuruvate carboxylase gene which is highly expressed during the phase of rapid fruit growth: this promoter has therefore been used to express or silence genes in tomato fruit as described previously [START_REF] Fernandez | Flexible tools for gene expression and silencing in tomato[END_REF][START_REF] Guillet | Regulation of the fruitspecific PEP carboxylase SlPPC2 promoter at early stages of tomato fruit development[END_REF] commercial practice in terms of plant nutrition and pest control. Plants were potted into 5Lpots containing potting compost. Water was supplied to plants using a drip irrigation system to maintain 20-30% drainage. The day/night temperature in the greenhouse was maintained at 22°C/16°C and relative humidity at 70-80%. Flowers were mechanically pollinated three times a week and side shoots removed as they appeared. Total fruit yield was estimated by a precise counting of the number of fruit per truss and weight of the fruits from the first six trusses. For the leaf removal experiment (carbon limitation) only 1 leaf was kept per truss, other leaves were removed (see Figure 1) once fruit were set. 6 plants per condition and genotype were used as replicates.

West Virginia 106

West Virginia 106 seeds (pmds lines and the mds5 control RNAi line) were selected on 0.5 x Murashige and Skoog medium with 100 mgL -1 kanamycin for the transgenic lines, and planted in potting compost in the greenhouse after 2-3 weeks under the same conditions as for the experiment with Moneymaker above. Total fruit yield was calculated per plant using a minimum of six plants per genotype. Fruits were harvested and counted once ripe using a maximum of three harvests. The average fruit weight was calculated per harvest and then per plant once harvesting was complete. The greenhouse culture of pmds lines was carried out between March and July 2017.

Fruits and leaves for biochemical assays and enzyme activity assays were quickly removed from the plants, fruit were cut and pericarps were selected and immediately frozen in liquid nitrogen and stored at -80°C before grinding to powder in liquid nitrogen. Harvesting of fruits and leaves was carried out at noon to minimise the effect of the hour of the day on ascorbate metabolism.

Enzyme activity assays

MDHAR activity was measured in leaves from 3-week old plantlets or ripe fruits. The assay of the activity of MDHAR enzyme was based on the oxidation of NADH at 30°C. Extractions were performed on ground powder conserved at -80 °C in 600 µl of 50 mM Tris-HCl at pH 7.8. 50 µl of extract was mixed with 1 mL of 1 mM ascorbate, 0.2 mM NADH and enough ascorbate oxidase to give a linear production of MDHA radical. Measurements were performed at 340 nm in triplicate. Measures of MDHAR activity represent the sum of activities of different MDHAR isoforms. A least four biological replicates per point were used, except for the pmds T0 lines when only one plant was available.

A C C E P T E D M A N U S C R I P T
Comment citer ce document : Truffault, V., Riqueau, G., Garchery, C., Gautier, H., Stevens, R. ( 2018). Is monodehydroascorbate reductase activity in leaf tissue critical for the maintenance of yield in tomato?. Journal of Plant Physiology, 222, 1-28. , DOI : 10.1016/j.jplph.2017.12.012

7

AO activity was measured on leaves from 3-week old plantlets. The assay was based on a previously described method (Pignocchi et al., 2003). Leaf material (50 mg) was homogenized with 0.1 M sodium phosphate at pH 5.6, 0.5 mM EDTA. Then 100 µL of extract was assayed rapidly at 265 nm in a final volume of 1 ml containing 0.1 mM ascorbate in the same phosphate buffer. The absorbance decrease at 265 nm was followed on addition of the extract. At least four biological replicates per point were used.

Ascorbate assay

Measurements of ascorbic acid content were carried out as described (Stevens et al., 2006).

Extractions were performed on ground powder conserved at -80 °C in ice-cold 6 % TCA. The spectrophotometric assay was based on the detection of dipyridyl-Fe 2+ complexes following the reduction of Fe 3+ to Fe 2+ by the reduced form of ascorbate present in the sample. Total ascorbate content (reduced + oxidized forms) was measured by mixing the sample with 5 mM dithiothreitol to reduce DHA, prior to the assay. Each extract was measured in duplicate. The specificity of the assay has been checked by comparison with other known methods (Stevens et al., 2006) and by using ascorbate oxidase to remove all ascorbate in order to detect and deduce background interference. At least four biological replicates per point were used.

Sugar assay

Measurements of glucose, fructose and sucrose were performed by HPLC, as previously described [START_REF] Gomez | A new procedure for extraction and measurement of soluble sugars in ligneous plants[END_REF]. Sugars were extracted in a methanol:chloroform:water mixture (1/0.6/1) at 4 °C. Polyvinylpolypyrrolidone was used to trap phenols and extracts were then purified before quantitative analysis. Alternatively glucose, fructose and sucrose were measured in samples using a microplate method and the R-Biopharm kit (Saint-Didierau-Mont-d'Or, France) according to the manufacturer's instructions.

Statistical analysis

Data were analysed using 'R' software (R Development-Core-Team, version 3.2.5). The data presented in Figures and Tables were submitted to a one or two-way analysis of variance (ANOVA) followed by a Tukey or Fisher test (as defined in the legends) to group data from the transgenic lines and wild type based on significant differences. Groups sharing the same letter are not considered to be statistically different.

Results

MDHAR silencing in Moneymaker does not affect yield

Firstly to characterize the transgenic lines, MDHAR activity was assayed on young leaves of plantlets of Moneymaker MDHAR silenced lines and compared to wild type (WT_MK).

MDHAR activity was reduced in all RNAi lines except one (MD-3MK), by at least 50% for MD-2MK, MD-6MK, MD-25MK and MD-26MK (Table 1). Ascorbate oxidase activity of the independent Moneymaker silenced lines (AO-2MK to AO-24MK) was also assayed in plantlets. All lines except two (AO-2MK and AO-3MK) showed a significant reduction in AO activity (Table 2). We measured ascorbate and dehydroascorbate content in plantlets of Moneymaker AO and MDHAR RNAi lines (supplementary Tables S1 andS2). We observed an increase in ascorbate content in 4 out of 6 AO lines (AO-3MK, AO-4MK, AO-22MK and AO-24MK), and no significant changes in dehydroascorbate content except in AO-24MK. In the MDHAR RNAi Moneymaker lines, no lines showed a significant increase in ascorbate or dehydroascorbate compared to wild type (samples harvested at 13h on a sunny day).

The effect on yield, fruit size and fruit number of a reduction in MDHAR or AO activity in Moneymaker was studied under optimal growth conditions and conditions of carbon limitation (removal of two leaves out of three; Figure 1). Our previous publication using West

Virginia 106 silenced for MDHAR showed that average fruit weight, and to a lesser extent yield, were reduced under control conditions and conditions of leaf removal compared to wild type (Table 3). In the case of Moneymaker MDHAR silenced lines, total fresh-weight fruit yield was not statistically different to wild type yield, when both wild type plants were considered, under optimal growth conditions (Figure 2A), and conditions of carbon limitation (Figure 2B). For each yield experiment, changes in average fruit weight are shown in Table 4 and the number of fruits per plant in Table 5. It is notable that the transformed plants showed a different response to wild type to carbon limitation as illustrated by comparing the lines under control and stress conditions (supplementary table S3). In this case we see that the yield ratio between the two conditions was higher than that the two wild types for all transgenic lines under-expressing MDHAR compared to the control condition. This observation is borne out by the statistical analysis which shows that four out of six of the MDHAR silenced lines were found in statistically different groups compared to control conditions.

Total fruit yield of Moneymaker AO silenced lines was surveyed under normal growth conditions and under the same conditions of carbon limitation described above. Under normal

A C C E P T E D M A N U S C R I P T
Comment citer ce document : Truffault, V., Riqueau, G., Garchery, C., Gautier, H., Stevens, R. ( 2018). Is monodehydroascorbate reductase activity in leaf tissue critical for the maintenance of yield in tomato?. Journal of Plant Physiology, 222, 1-28. , DOI : 10.1016/j.jplph.2017.12.012 9 growth conditions, Moneymaker AO silenced line AO-24MK was the only line showing differences in term of yield with the wild type (Figure 3 and supplementary Table S4), due to a reduced fruit weight (Table 6), the five other independent lines all showing yield that was not statistically different to either wild type group. When the plants were grown under a situation mimicking carbon stress, the only transgenic line to show a reduced yield phenotype was AO-21MK due to a combination of a lower fruit weight and a reduced number of fruits per truss (Table 7), none of the other five lines were different to wild type. When compared to previous work carried out in cherry tomato, these results highlight the genetic variability in the response of yield and ascorbate metabolism to MDHAR or AO activities. Thus, we went more deeply into possible explanations linked to the source-sink relationship using a fruit specific promoter.

In cherry tomato, modification of MDHAR activity induces changes in yield which are not correlated with MDHAR activity in fruit

We studied the hypothesis that whole plant MDHAR activity was more important than fruit MDHAR activity in the establishment of yield by silencing MDHAR activity in fruit compared to the whole plant. In Figure 4, we see that total fruit yield per plant is unaffected in lines where MDHAR activity is silenced in fruit only (up to 9% of remaining fruit enzymatic activity, see Materials and methods), as no significant difference was apparent between these lines and controls (WT and T-). In contrast, MDHAR silenced line mds5 showed a strong reduction in total fruit yield per plant (50% of WT): this is due to a smaller fruit weight (significant for mds5) and a lower number of total fruits (supplementary Table S5).

Discussion

MDHAR activity does not affect yield in large-fruited tomato

The process of domestication of tomato has been characterised by major changes in plant anatomy, notably a large increase in fruit size and weight which is apparently attributable to a few loci (Nesbitt and Tanksley, 2002). These loci often correspond to changes affecting the regulation of genes controlling flower anatomy: for example, carpel number or cell number [START_REF] Chakrabarti | A cytochrome P450 regulates a domestication trait in cultivated tomato[END_REF][START_REF] Frary | fw2.2: a quantitative trait locus key to the evolution of tomato fruit size[END_REF] but plant hormones also affect fruit size [START_REF] Ariizumi | Genes that influence yield in tomato[END_REF]. Our previous work on cherry tomato has shown that slower early growth and a decrease in yield are associated with MDHAR silencing and that such phenotypes were more pronounced under carbon limitation (Truffault et al., 2016). Differences in yield were also obtained with ascorbate oxidase silenced lines, which, in contrast to MDHAR, show improved yield under conditions of carbon limitation [START_REF] Garchery | A diminution in ascorbate oxidase activity affects carbon allocation and improves yield in tomato under water deficit[END_REF]. The average fruit weight of this genotype is less than 10g. The aim of the current study was to compare phenotypes in a large-fruited cultivated tomato variety for which we chose Moneymaker (average fruit weight above 50g). We suggest that the genetic background might play a role in the phenotypes as the harvest index (ratio of fruit yield to total crop weight) increases considerably when comparing a variety like Moneymaker with cherry tomato and therefore the mechanisms to distribute carbon between source and sink tissues are probably different.

Fruit yield of the Moneymaker transgenic lines was not different to wild type under normal growth conditions or carbon stress for either Moneymaker MDHAR or Moneymaker AO silenced lines (Figures 2 and3). In the case of Moneymaker AO silenced lines, we did not notice any modifications in terms of yield, number of fruits per truss or fruit weight under either normal or carbon-limited conditions (Figure 3) in contrast to results in cherry tomato [START_REF] Garchery | A diminution in ascorbate oxidase activity affects carbon allocation and improves yield in tomato under water deficit[END_REF]. We had hypothesized that in cherry tomato MDHAR silencing led to reduced growth of sink organs (fruits and roots) because they did not receive enough carbon from source organs i.e. photosynthetic leaves but for the moment we are unable to distinguish between the hypotheses that this is because (i) less carbon is available or (ii) the same amount of carbon is available but it is distributed differently between source and sink organs. It also presumably means that under control conditions in Moneymaker carbon is not limiting and no phenotype is seen as the 'source' carbon and/or its transport to fruits is sufficient to overcome the disadvantage of silencing MDHAR. No significant changes in soluble sugar content were observed in leaves or fruit in this study (Supplementary Tables S7 andS8), and although we did not examine early growth in our Moneymaker transgenic lines, we could not detect any differences in vegetative growth of mature plants under optimal conditions (Supplementary [START_REF] Garchery | A diminution in ascorbate oxidase activity affects carbon allocation and improves yield in tomato under water deficit[END_REF]Truffault et al., 2016).

MDHAR and AO activity and regulation of ascorbate content

Ascorbate recycling is particularly linked to stress responses and also to growth as summarized by [START_REF] Gallie | The role of L-ascorbic acid recycling in responding to environmental stress and in promoting plant growth[END_REF]. The ascorbate biosynthesis pathway is responsible for the accumulation of ascorbate in cells, but over a time-scale of several hours [START_REF] Bartoli | Interrelationships between light and respiration in the control of ascorbic acid synthesis and accumulation in Arabidopsis thaliana leaves[END_REF].

The improvement of ascorbate recycling by genetic tools has been well studied as efficient recycling might be the key to adapt to sudden changes in the environment. MDHAR is important as the enzyme is localized in multiple cellular compartments [START_REF] Lisenbee | Arabidopsis peroxisomes possess functionally redundant membrane and matrix isoforms of monodehydroascorbate reductase[END_REF] and its activity allows quick recycling of the radical MDHA into reduced ascorbate. In previous experiments [START_REF] Gest | Light-dependent regulation of ascorbate in tomato by a monodehydroascorbate reductase localized in peroxisomes and the cytosol[END_REF], we revealed a light-dependent negative correlation between MDHAR activity and ascorbate content in cherry tomato in both leaves and fruits, as also been described by Ren et al (2015). In the present study, we analyzed potential negative control of the ascorbate pool by MDHAR activity in Moneymaker, however we did not observe any significant change in ascorbate content compared to wild type, in contrast to previous results from cherry tomato (West Virginia 106) or Ailsa Craig [START_REF] Gest | Light-dependent regulation of ascorbate in tomato by a monodehydroascorbate reductase localized in peroxisomes and the cytosol[END_REF][START_REF] Haroldsen | Constitutively expressed DHAR and MDHAR influence fruit, but not foliar ascorbate levels in tomato[END_REF]. However, AO activity does seem to be negatively correlated to ascorbate content in this study. DHAR activity often seems to be correlated to ascorbate content in tobacco, maize, potato, tomato and Arabidopsis [START_REF] Chen | Increasing vitamin C content of plants through enhanced ascorbate recycling[END_REF][START_REF] Haroldsen | Constitutively expressed DHAR and MDHAR influence fruit, but not foliar ascorbate levels in tomato[END_REF]Qin et al., 2011;[START_REF] Li | Overexpression of chloroplastic monodehydroascorbate reductase enhanced tolerance to temperature and methyl viologen-mediated oxidative stresses[END_REF]. MDHAR's influence on ascorbate content still remains unclear as ascorbate content is not always correlated to MDHAR activity in the samples, this might be because MDHA enzymatic metabolism (AO and MDHAR notably, but it should be relevant for APX also) is more likely related to stress tolerance and growth processes than to the regulation of ascorbate content or because of feedback control on the quantities of these metabolites.

Fruit MDHAR activity does not affect yield in cherry tomato, in contrast to whole plant

MDHAR activity

Given the link between ascorbate recycling and yield shown in cherry tomato (Truffault et al., 2016), we tested the importance of leaf, or whole plant, ascorbate recycling activities, and

A C C E P T E D M A N U S C R I P T
Comment citer ce document : Truffault, V., Riqueau, G., Garchery, C., Gautier, H., Stevens, R. ( 2018). Is monodehydroascorbate reductase activity in leaf tissue critical for the maintenance of yield in tomato?. Journal of Plant Physiology, 222, 1-28. , DOI : 10.1016/j.jplph.2017.12.012 12 therefore source metabolism, compared to fruit ascorbate recycling activity in the establishment of yield in tomato. Lines under-expressing MDHAR3 at the whole plant level, are characterized by a lower yield than the wild type, which is linked to a reduction in fruit cell size and weight in cherry tomato (Truffault et al., 2016) and confirmed in the present study with mds5. The opposite phenotype has been observed for AO RNAi lines (Garchery et al., 2013). One possibility is that hormonal metabolism is perturbed in our transgenic lines, auxin being a good candidate. Ascorbate recycling and oxidation are often related to growth processes [START_REF] Fry | Oxidative scission of plant cell wall polysaccharides by ascorbate-induced hydroxyl radicals[END_REF][START_REF] González-Reyes | Stimulation of onion root elongation by ascorbate and ascorbate free radical in Allium cepa L[END_REF][START_REF] Kato | Changes in ascorbate oxidase gene expression and ascorbate levels in cell division and cell elongation in tobacco cells[END_REF]. Using a fruit specific promoter in cherry tomato West Virginia 106, we did not observe any reduction in yield, in contrast to the smaller fruit and lower number of fruits harvested when whole plant activity is modified. Early growth of tomato plantlets was also affected by a reduction in MDHAR activity (Truffault et al., 2016), so it is predominantly sink tissues and organs that are affected. This observation reveals that yield and MDHAR activity in autotrophic tissue (leaves) is more likely to be linked than the activity of sink tissues. Interestingly, auxin biosynthesis has been reported only in vegetative organs and roots (Ljung et al., 2001), but not yet in fruits. In the case of our RNAi lines, a perturbation of leaf MDHAR metabolism may have induced changes in auxin signaling, or metabolism. Another possible link that has been made is the quantity, availability or transport of sugars (photoassimilate) from source to sink organs. An example of a redox enzyme with effects on growth and photochemistry (and therefore leaf chemistry) is DHAR: Chen and Gallie (Chen andGallie, 2006, 2008) have

shown that this enzyme is required for growth and efficient photochemistry, and suggest that DHAR maintains photosynthetic function.

Conclusion

Moneymaker responds differently to cherry tomato to a reduction in MDHAR or AO activity as effects on yield are not seen under normal conditions or conditions of carbon deficit.

Moneymaker therefore seems to be less affected by the changes in the activity of these enzymes than the cherry tomato genotype West Virginia. We have also shown that MDHAR activity in leaf tissue is critical for the establishment of yield as a reduction in fruit MDHAR activity has no impact on yield in cherry tomato. Further studies will be necessary to understand how carbon is accumulated and distributed in these plants following modification of ascorbate recycling enzymes. Genotypic-specific effects on yield stability also point to a source of genetic variability in the response to the ascorbate redox state. Plants were grown in a greenhouse in Autumn under normal growth conditions (A) or carbonlimiting conditions (B). The treatment consisted in removing 2 out of 3 leaves. 4 plants per genotype and per condition were used. Total fruit yield per plant (in grams) was calculated from 6 trusses per plant. Data were analysed together and submitted to a two-way analysis of variance (ANOVA) followed by a Fisher test (p<0.05) but no statistical differences could be noticed between genotype (p-value=0.622) under each growth conditions. Complete statistical analysis is available in supplementary Table S3 showing a strong treatment effect (p-value= 8.79e-09). Error bars represent standard deviation. grown under optimal growth conditions. At least, six plants per genotype were used. Data

were analysed together and submitted to a one-way analysis of variance (ANOVA) followed by a Fisher test (p<0.05). Complete statistical analysis is available in supplementary Table S5.

Error bars represent standard deviation. Average fruit weight (in grams) from 6 trusses per plant harvested from Moneymaker plants (two wild types and six independent RNAi AO transgenic lines AO-2MK to AO-24MK).

Plants were grown in a greenhouse in Spring under normal growth conditions (Control) or carbon-limiting conditions (Leaf removal). Data were analyzed as indicated in Table 4.

  Comment citer ce document : Truffault, V., Riqueau, G., Garchery, C., Gautier, H., Stevens, R. (2018). Is monodehydroascorbate reductase activity in leaf tissue critical for the maintenance of yield in tomato?. Journal of Plant Physiology, 222, 1-28. , DOI : 10.1016/j.jplph.2017.12.012 3

  Comment citer ce document : Truffault, V., Riqueau, G., Garchery, C., Gautier, H., Stevens, R. (2018). Is monodehydroascorbate reductase activity in leaf tissue critical for the maintenance of yield in tomato?. Journal of Plant Physiology, 222, 1-28. , DOI : 10.1016/j.jplph.2017.12.012 8

  Comment citer ce document : Truffault, V., Riqueau, G., Garchery, C., Gautier, H., Stevens, R. (2018). Is monodehydroascorbate reductase activity in leaf tissue critical for the maintenance of yield in tomato?. Journal of Plant Physiology, 222, 1-28. , DOI : 10.1016/j.jplph.2017.12.012 10

Figure legends Figure 1 Figure 2

 legends12 Figure legends Figure 1 Photos of 70 day-old mature wild-type Moneymaker plants to illustrate a plant grown under conditions of normal culture (left panel) and a plant subjected to carbon stress/carbon limitation by removal of 2 out of three leaves (right panel). Moneymaker plants for the yield experiments were grown in a Venlo-type greenhouse located in Avignon (44°N), France (sowing occurred on the 1 st August 2012 and planting took place on the 12 th September 2012).Plants were potted into 5L-pots containing potting compost. Water was supplied to plants using a drip irrigation system to maintain 20-30% drainage. The day/night temperature in the greenhouse was maintained at 22°C/16°C and relative humidity at 70-80%.

Figure 3 Figure 4

 34 Figure 3Graph showing total fruit yield per plant (in grams) from 5 trusses per plant harvested from Moneymaker plants (two wild types and six independent RNAi AO transgenic lines AO-2MK to AO-24MK). Plants were grown in a greenhouse in Spring under normal growth conditions (A) or carbon-limiting conditions (B). The treatment consisted in removing 2 out of 3 leaves once fruit were set. 6 plants per genotype and per condition were used. Data were analysed together and submitted to a two-way analysis of variance (ANOVA) followed by a Fisher test (p<0.05). Complete statistical analysis is available in supplementary TableS4highlighting the treatment effect (p-value= 0.00513). Error bars represent standard deviation.

  Comment citer ce document : Truffault, V., Riqueau, G., Garchery, C., Gautier, H., Stevens, R. (2018

  RNAi construct described inGarchery et al. (2013) (Solyc04g054690). The presence of the relevant transgene was verified by PCR. The six independent RNAi lines silenced for the MDHAR isoform used in this paper were MD-2MK, MD-3MK, MD-6MK, MD-25MK, MD-

	26MK and MD-29MK. Moneymaker RNAi lines for AO were labeled AO-2MK, AO-3MK,
	AO-4MK, AO-21MK, AO-22MK, AO-24MK. Lines studied were from the T1 generation,
	selected on kanamycin and were heterozygous or homozygous for the transgene and selected
	based on the reduction in enzymatic activity. Wild type plants were used as controls,
	U WT_MK1 and WT_MK2, were from two separate seed bulks and treated independently during the experiments. 2. West Virginia 106 Solanum lycopersicum L. cv West Virginia 106 (cherry tomato) was transformed with the S C R I P T RNAi MDHAR construct (see above) described in Gest
	N
	A
	M
	C C E P T E D
	A
	(Solyc09g009390) and the AO
	5
	Comment citer ce document :
	Truffault, V., Riqueau, G., Garchery, C., Gautier, H., Stevens, R. (2018). Is
	monodehydroascorbate reductase activity in leaf tissue critical for the maintenance of yield in tomato?. Journal
	of Plant Physiology, 222, 1-28. , DOI : 10.1016/j.jplph.2017.12.012

table S6

 S6

	U S C R I P T
	N
	A
	M
	). Further hypotheses that could explain the phenotypes involve the mechanisms of C C E P T E D
	hormonal control of fruit set and development in Moneymaker: auxin and gibberellin can A
	regulate fruit set and development (de Jong et al., 2009a; de Jong et al., 2009b) and links
	between ascorbate metabolism and fruit set have been highlighted (Attolico and De Tullio,

2006;

[START_REF] Kotchoni | Alterations in the endogenous ascorbic acid content affect flowering time in Arabidopsis[END_REF]

. One possibility is that the ascorbate molecule is necessary as a cofactor during the biosynthesis of gibberellin and auxin

(Pastori et al., 2003)

, its concentration or redox state could therefore alter flowering and fruit set. Another possibility is Comment citer ce document : Truffault, V., Riqueau, G., Garchery, C., Gautier, H., Stevens, R. (2018). Is monodehydroascorbate reductase activity in leaf tissue critical for the maintenance of yield in tomato?. Journal of Plant Physiology, 222, 1-28. , DOI : 10.1016/j.jplph.2017.12.012 11 that changes occurred in photosynthesis: in our previous work we showed decreased photosynthesis in cherry tomato MDHAR RNAi lines but no significant different in ascorbate oxidase RNAi lines in cherry tomato. Stomatal conductance also changed, increasing in AO RNAi lines and decreasing in MDHAR RNAi lines

Table 1

 1 ). Is monodehydroascorbate reductase activity in leaf tissue critical for the maintenance of yield in tomato?. Journal of Plant Physiology, 222, 1-28. , DOI : 10.1016/j.jplph.2017.12.012 21 MDHAR activity measured in 21-day-old Moneymaker plantlets. Plantlets of wild type (WT_MK) and MDHAR silenced lines (MD-2MK to MD-29MK) plantlets were grown in a greenhouse in Spring. 6 plantlets per genotype were used as replicates. MDHAR activity is expressed in µmol NADH oxidized min -1 gFw -1 . Different letters indicate different groups as defined by a Tukey test (p<0.05), se represents standard error.

	Line	MDHAR activity (µmol min -1 g -1 )	se	p<0.05
	WT_MK	0.20	±0.04	a
	MD-2MK	0.09	±0.02	c
	MD-3MK	0.15	±0.04	ab
	MD-6MK	0.08	±0.01	c
	MD-25MK	0.08	±0.02	c
	MD-26MK	0.08	±0.01	c
	MD-29MK	0.10	±0.01	bc

Comment citer ce document : Truffault, V., Riqueau, G., Garchery, C., Gautier, H., Stevens, R. (

2018

). Is monodehydroascorbate reductase activity in leaf tissue critical for the maintenance of yield in tomato?. Journal of Plant Physiology, 222, 1-28. , DOI : 10.1016/j.jplph.2017.12.012 22

Table 2

 2 Ascorbate oxidase activity in 21 days old Moneymaker plantlets. Plantlets of wild type (WT_MK) and AO silenced lines (AO-2MK to AO-24MK) plantlets were grown in a greenhouse in Spring. 6 plantlets per genotype were used as replicates. AO activity is expressed in µmol ascorbate oxidized min -1 gFw -1 . Different letters indicate different groups as defined by a Tukey test (p<0.05), se represents standard error.

	Line	AO activity (µmol min -1 g -1 )	se	p
	WT_MK	2.23	±0.15	a
	AO-2MK	1.87	±0.20 ab
	AO-3MK	2.30	±0.03	a
	AO-4MK	1.66	±0.13 b
	AO-21MK	1.57	±0.32 b
	AO-22MK	1.56	±0.08 b
	AO-24MK	1.47	±0.13 b

Comment citer ce document : Truffault, V., Riqueau, G., Garchery, C., Gautier, H., Stevens, R. (

2018

). Is monodehydroascorbate reductase activity in leaf tissue critical for the maintenance of yield in tomato?. Journal of Plant Physiology, 222, 1-28. , DOI : 10.1016/j.jplph.2017.12.012 23

Table 3

 3 Average total fruit yield per plant (in grams), fruit number per plant and average fruit weight (in grams) from 6 trusses per plant harvested from West Virginia 106 plants previously

	described (wild type and two independent RNAi MDHAR transgenic lines). Plants were
	grown in a greenhouse under normal growth conditions (control) or carbon-limiting
	conditions (leaf removal). The treatment consisted in removing 2 out of 3 leaves once fruit
	were set. At least six plants per genotype and per condition were used. Data were analysed
	together and submitted to a two-way analysis of variance (ANOVA) followed by a Fisher test (p<0.05). Treatment genotype Total fruit yield per plant (g) standard error Fisher statistical groups Control WT 383.3 ±21.0 a Control mds3 293.8 ±27.7 b Control mds5 300.8 ±34.7 Leaf Removal WT 311.5 ±39.0 ab Leaf Removal mds3 259.0 ±13.3 Leaf Removal mds5 155.8 ±22.2 c Treatment genotype Number of fruit per plant standard error statistical groups M Fisher A b N b U S C R I P T
	Control Control Control Leaf Removal Leaf Removal Leaf Removal Treatment Control Control Control Leaf Removal A C C E P T E D WT 56.5 ±2.9 mds3 52.0 ±1.9 mds5 62.7 ±3.0 WT 47.3 ±4.8 mds3 50.5 ±1.7 mds5 49.0 ±3.1 genotype Average fruit weight (g) standard error WT 6.8 ±0.2 mds3 5.6 ±0.3 mds5 4.8 ±0.5 WT 6.6 ±0.3	ab bc a c bc bc statistical Fisher groups a bc ab c
	Leaf Removal	mds3	5.1 ±0.2	c
	Leaf Removal	mds5	3.8 ±0.2	d

Comment citer ce document : Truffault, V., Riqueau, G., Garchery, C., Gautier, H., Stevens, R. (

2018

). Is monodehydroascorbate reductase activity in leaf tissue critical for the maintenance of yield in tomato?. Journal of Plant Physiology, 222, 1-28. , DOI : 10.1016/j.jplph.2017.12.012 24

Table 4

 4 Average fruit weight (in grams) from 6 trusses per plant harvested from Moneymaker plants (two wild types and six independent RNAi MDHAR transgenic lines MD-2MK to MD-29MK). Plants were grown in a greenhouse in Autumn under normal growth conditions Comment citer ce document : Truffault, V., Riqueau, G., Garchery, C., Gautier, H., Stevens, R. (2018). Is monodehydroascorbate reductase activity in leaf tissue critical for the maintenance of yield in tomato?. Journal of Plant Physiology, 222, 1-28. , DOI : 10.1016/j.jplph.2017.12.012 25

	(control) or carbon-limiting conditions (leaf removal). The treatment consisted in removing 2
	out of 3 leaves once fruit were set. 4 plants per genotype and per condition were used. Data
	were analysed together and submitted to a two-way analysis of variance (ANOVA) followed
	by a Fisher test (p<0.05). Treatment genotype Control WT_MK1 Control WT_MK2 Control MD-2MK Control MD-3MK Control MD-6MK Control MD-25MK Control MD-26MK Control MD-29MK	Average fruit weight (g) 53.7 ±5.0 standard error 50.8 ±1.2 44.3 ±7.1 36.9 ±5.7 37.9 ±3.1 45.8 ±5.7 38.6 ±2.3 48.5 ±3.3 A N U S C R I P T Fisher statistical groups ab abc abc c abc abc bc bc
	Leaf Removal Leaf Removal Leaf Removal Leaf Removal Leaf Removal Leaf Removal Leaf Removal Leaf Removal C C E P T E D WT_MK1 WT_MK2 MD-2MK MD-3MK MD-6MK MD-25MK MD-26MK MD-29MK M	43.6 ±7.4 46.0 ±4.6 57.4 ±11.3 38.1 ±5.6 42.0 ±8.9 35.9 ±3.3 46.5 ±1.6 46.3 ±5.2	abc abc abc a bc abc c abc
	A

Table 5

 5 Number of fruit on 6 trusses per plant harvested from Moneymaker plants (two wild types and six independent RNAi MDHAR transgenic lines MD-2MK to MD-29MK). Data were analyzed as indicated in Table4.Comment citer ce document : Truffault, V., Riqueau, G., Garchery, C., Gautier, H., Stevens, R. (2018). Is monodehydroascorbate reductase activity in leaf tissue critical for the maintenance of yield in tomato?. Journal of Plant Physiology, 222, 1-28. , DOI : 10.1016/j.jplph.2017.12.012 26

	Treatment	genotype	Number of fruits per plant	standard error	Fisher statistical groups
	Control	WT_MK1	24.2 ±4.6	cdef
	Control Control Control Control Control Control Control Leaf Removal Leaf Removal Leaf Removal	WT_MK2 MD-2MK MD-3MK MD-6MK MD-25MK MD-26MK MD-29MK WT_MK1 WT_MK2 MD-2MK	26.2 ±5.1 27.7 ±2.9 29.7 ±5.0 38.2 ±4.7 30.5 ±5.0 37.0 ±2.5 27.7 ±1.4 23.0 ±2.5 23.2 ±3.8 15.5 ±5.9 U S C R I P T abcde abcd abc a abc ab abcd cdef cdef def
	Leaf Removal Leaf Removal Leaf Removal Leaf Removal Leaf Removal	MD-3MK MD-6MK MD-25MK MD-26MK MD-29MK	14.0 ±4.6 24.5 ±2.1 23.5 ±1.8 20.2 ±3.9 12.7 ±2.2 A M N	ef cdef f cdef cdef
	C C E P T E D
	A

Table 6

 6

Table 7

 7 Comment citer ce document : Truffault, V., Riqueau, G.,Garchery, C., Gautier, H., Stevens, R. (2018). Is monodehydroascorbate reductase activity in leaf tissue critical for the maintenance of yield in tomato?. Journal of Plant Physiology, 222, 1-28. , DOI : 10.1016/j.jplph.2017.12.012 27 Number of fruit on 6 trusses per plant harvested from Moneymaker plants (two wild types and six independent RNAi AO transgenic lines AO-2MK to AO-24MK). Data were analyzed as indicated in Table 4.

	Treatment	genotype	Average fruit weight (g)	standard error	Fisher statistical groups
	Control Control Control Control Control Control Control Control Leaf Removal Leaf Removal Leaf Removal Leaf Removal Leaf Removal Leaf Removal Leaf Removal	WT_MK1 WT_MK2 AO-2MK AO-3MK AO-4MK AO-21MK AO-22MK AO-24MK WT_MK1 WT_MK2 AO-2MK AO-3MK AO-4MK AO-21MK AO-22MK	73.6 ±8.3 76.3 ±7.8 63.8 ±2.6 74.6 ±6.9 55.9 ±7.8 64.6 ±12.8 75.9 ±7.1 52.0 ±1.8 68.4 ±4.4 64.9 ±6.0 60.6 ±7.4 68.6 ±7.4 55.0 ±6.9 75.9 ±9.1 A M 56.0 ±2.4 N U S C R I P T ab a abc ab abc abc a c abc abc abc bc a abc abc
	Leaf Removal C C E P T E D AO-24MK	62.7 ±8.3	abc
	A

Acknowledgements

We thank the greenhouse staff at INRA Avignon, particularly Karine Pellegrino, for technical assistance. The PhD of Vincent Truffault was financed by INRA and the Provence-Alpes-Côte d'Azur region. The work was financed by the ANR-Bioadapt project ANR-13-ADAP-0013.


Hadi Abdi Khojasteh

Ebrahim Ansari
email: ansari@iasbs.ac.ir

Mahdi Bohlouli
email: bohlouli@iasbs.ac.ir

LSCP: Enhanced Large Scale Colloquial Persian Language Understanding

Keywords: Persian Corpus, Large Scale Language Understanding, Colloquial Persian Language, Multilingual, Informal Language

Language recognition has been significantly advanced in recent years by means of modern machine learning methods such as deep learning and benchmarks with rich annotations. However, research is still limited in low-resource formal languages. This consists of a significant gap in describing the colloquial language especially for low-resourced ones such as Persian. In order to target this gap for low resource languages, we propose a "Large Scale Colloquial Persian Dataset" (LSCP). LSCP is hierarchically organized in a semantic taxonomy that focuses on multi-task informal Persian language understanding as a comprehensive problem. This encompasses the recognition of multiple semantic aspects in the human-level sentences, which naturally captures from the real-world sentences. We believe that further investigations and processing, as well as the application of novel algorithms and methods, can strengthen enriching computerized understanding and processing of low resource languages. The proposed corpus consists of 120M sentences resulted from 27M tweets annotated with parsing tree, part-of-speech tags, sentiment polarity and translation in five different languages.

Introduction

Language understanding is a comprehensive problem that encompasses the recognition of multiple semantic aspects of relations between different linguistic units and compounds: homonymy, synonymy, antonymy, hypernymy, hyponymy, meronymy, metonymy, holonymy, paronyms. In other hands, large scale corpora are one of the key resources in Natural Language Processing (NLP). In spite of their importance in many lingual applications, no large-scale Persian corpus has been made available so far, given the difficulties in its creation and the intensive labour required due to its low resources, especially in colloquialism. Today, modern machine learning algorithms such as deep neural networks provide improved processing performance for a wide variety of applications, mainly text mining and NLP. But, they need large scale datasets, specially for training and pre-processing of the algorithms. It should also be mentioned that collecting, preparing, pre-processing and annotating large scale datasets from social media and web based materials such as product reviews are not easy task. Because most of them are associated with the mixed multilingual and informal texts that need better understanding. Social networks such as Twitter deliver large volume of disparate data including short and long mixed and informal text consisting symbols, other languages and images. The quality of machine translation depends heavily on availability of various datasets. In this regard, low resourced languages such as Persian lack the quality of machine translation due to missing enriched datasets. It should be stated that releasing enhanced dataset for low resourced languages can also support the use of new methods such as Neural Machine Translation (NMT) in these languages in order to improve their performance and accuracy and also enhance rule-based and statistical methods.

؟ ی کی

? your home come when

Towards the Persian Colloquial Language

The language in oral form is typically much more dynamic than its written. The written variety of a language typically involves a higher level of ritual, whereas the spoken form is characterised by several contractions and abbreviations. In formal written texts, longer and tougher sentences tend to be used, as a result of the reader can re-read the troublesome parts, if they lose track. The spoken form is shorter, additionally thanks to semantic augmentation by visual cues that don't seem to be available in the written forms. The size of vocabulary in use is one of the foremost noticeable variations between oral and written sorts of discourse. The written language uses synonyms rather than continuance an equivalent word over and all over again. This is, however, not the case in oral language, which typically uses a lot of restricted vocabulary. The extent of difficulty in pronunciation may additionally have an effect on the words chosen.

Oral languages tend to use words of fewer syllables.

In addition to aforesaid general differences between spoken and written varieties of languages, the Persian language introduces range of variations that further expand this gap. Additionally, several informal words are not applicable to be used in formal language, so that there are outstanding variations in pronunciation of words. This alteration is quite common, but has no rule. The speaker is not allowed to interchange various words in the colloquial language. Obviously, Persian tweets involve all of earlier stated features of spoken form of it, which is rare in formal writings.

Related Work

Nowadays, almost all textual data for NLP are based on social media and online sources such as retrieved ones from the web. The social media generated data is, in fact, informal, multilingual and mixed, which needs complex methods to understand them. This problem becomes even more difficult, when it comes to low resource languages such as Persian. In this regard, we review the literature first for informal language understanding and then concentrate on Persian language datasets.

2.1. Informal Language Understanding Datasets [START_REF] Lo | Multilingual sentiment analysis: from formal to informal and scarce resource languages[END_REF] argued that the social media data is typically multilingual and it is mostly difficult to grasp the full sentiment of online user-generated product content specially from social networks. They already used usergenerated product reviews as an informal language datasets and also focused on the mixed linguistic origin of online social media languages and accordingly reviewed existing tools and state-of-the-art research on sentiment analysis works and also provided recommendations in this regard. [START_REF] Zhao | Lsicc: A large scale informal chinese corpus[END_REF] proposed a large scale informal Chinese Corpus. This dataset consists of 37M book reviews and 50 thousand netizen's comments.

Persian Language Understanding Datasets

2.2.1. Part-of-Speech Tagger Bijankhan corpus [START_REF] Bijankhan | The role of the corpus in writing a grammar: An introduction to a software[END_REF]) is a tagged corpus that is suitable for natural language processing research on the Persian language. This collection is gathered form daily news and common texts. In this collection all documents are categorized into different subjects such as political, cultural and so on. Totally, there are 4300 different subjects. The Bijankhan collection contains about 2.6M manually tagged words with a tag set that contains 40 Persian POS tags. FarsNet [START_REF] Shamsfard | Semi automatic development of farsnet; the persian wordnet[END_REF]) is a lexical ontology for the Persian language that is designed to contain a Persian WordNet with about more than 30K entries organized in about 20K synsets and verbs argument structures with their selectional restrictions. MULTEXT-East [START_REF] Erjavec | Multext-east: morphosyntactic resources for central and eastern european languages[END_REF]) is a multilingual dataset for language engineering research and development. This dataset contains word-forms, lemmas and MSDs for about 2M words. Uppsala Persian Dependency Treebank (UPDT) [START_REF] Seraji | Morphosyntactic Corpora and Tools for Persian[END_REF] is a dependency-based syntactically an notated corpus. Persian Morphologically Segmented Lexicon [START_REF] Ansari | Persian morphologically segmented lexicon 0.5. lindat/clarin digital library at the institute of formal and applied linguistics (úfal)[END_REF] dataset includes 45300 Persian word forms which are manually segmented into sequences of morphemes.

Named Entity Recognition and Dependency

Parsing Persian Syntactic Dependency Treebank [START_REF] Rasooli | Development of a persian syntactic dependency treebank[END_REF] has 29,982 annotated sentences including samples from almost all verbs of the Persian valency lexicon. ArmanPersoNERCorpus [START_REF] Hafezi | Neural architecture for persian named entity recognition[END_REF] includes 250K tokens and 7K Persian sentences in total and is available in 3 folds to be used in turn as training and test sets contains tokens, along with its manually annotated namedentity and Named Entity Recognition tags. Universal Dependencies [START_REF] Nivre | Universal dependencies 2.0. lindat/clarin digital library at the institute of formal and applied linguistics, charles university[END_REF] is a cross-linguistically consistent corpus that holds, with the goal of facilitating multilingual parser development, cross-lingual learning, and parsing research from a language typology perspective.

Text Categorization and Spell Checking

FASpell [START_REF] Barari | Clonizer spell checker adaptive language independent spell checker[END_REF] dataset was developed for the evaluation of spell checking algorithms. It contains a set of pairs of misspelled Persian words and their corresponding corrected forms similar to the ASpell dataset used for English. Hamshahri collection [START_REF] Aleahmad | Hamshahri: A standard persian text collection[END_REF]) is a standard reliable Persian text collection that was used at Cross Language Evaluation Forum (CLEF) for evaluation of Persian information retrieval systems.

Machine Translation

OPUS [START_REF] Tiedemann | Parallel data, tools and interfaces in opus[END_REF] is a growing collection of translated texts from the web. In the OPUS project try to convert and align free online data, to add linguistic annotation, and to provide the community with a publicly available parallel corpus. English-Persian Parallel Corpus [START_REF] Karimi | Extracting an english-persian parallel corpus from comparable corpora[END_REF]) consists of about 200K bilingual paired sentences which have been sorted by their degree of similarity. Tehran English-Persian Parallel (TEP) Corpus [START_REF] Pilevar | Tep: Tehran english-persian parallel corpus[END_REF] is a English-Persian corpus constructed using movie subtitles from 21K files. W2C [START_REF] Majliš | W2c-web to corpus-corpora[END_REF] corpora is a set of corpora for 120 languages automatically collected from Wikipedia and the web. dotIR [START_REF] Darrudi | dorir collection for persian web retrieval[END_REF]) is a standard Persian test collection that is suitable for evaluation of web information retrieval algorithms contains Persian web pages including their text, links and metadata. It is A good test bed for evaluation of link based information retrieval algorithms and also includes enough queries for a valid evaluation.

LSCP Dataset

The Large Scale Colloquial Persian Language Understanding dataset (LSCP) is organized hierarchically in a semantic taxonomy of colloquialism understanding. Almost, all realwold conditioned Persian datasets are targeting semantic analytics of formal language. However, a language is not only formal, which provides a clear, properly framed and well organised. By focusing on formal writing, we ignore the information about abbreviations, unspecified vocabulary, contractions, jargons, slang, cliche, colloquial diction and ordinary sentences. To our knowledge, there is no colloquial large scale corpus publicly available. The LSCP has 120M sentences from 27M casual Persian sentences with its derivation tree, part-of-speech tags, sentiment polarity and translations in English, German, Czech, Italian and Hindi spoken languages. Figure 1 shows a sample of the extracted dataset with its annotation. One of the important research questions which is not addressed well in recent works on language learning, is leveraging the informal language information in sentences. The LSCP dataset makes it possible to assess the effect of learning and knowledge transfer among different tasks, such as enabling transfer learning of language in formal to informal and vice-versa or lowresource cross-lingual transfer. In summary, the LSCP can help the NLP community and bring more interesting solutions to colloquial language.

LSCP Statistics

The LSCP consists of 27M tweets. The number of samples for train, validation, and test splits are reported in Table 1. The dataset consists of usual sentences. In practice, the length of the sentences are different with a maximum of 40 words length. LSCP contains human language texts into lists of sentences and words, generated base forms of those words, their parts of speech (Table 2), a syntactic structure dependency parse (Table 3) for each word and translation in five languages as it is shown in Figure 2.

Collecting and Annotation

Building a large-scale language understanding dataset is a time-consuming task. In practice, there are two main tasks, which are usually most time consuming for creating a largescale corpus: (a) data collection and (b) annotation. We collected 27M tweets by a crawler [START_REF] Abdi Khojasteh | Deep multimodal image-text embeddings for automatic cross-media retrieval[END_REF] based on the Twitter API. To make sure that the collection is diverse, we first created a list of seed users. Then, the followers of the seed accounts were added to the list. Next, the latest tweets of the users in our list were extracted and saved in the dataset. To make the data appropriate for our task, we removed the same tweets, non-Persian tweets and the ones that had fewer words. This led the dataset to have relatively long sentences with diverse concept.

For the annotation of the datasets, we adopt a semiautomatic crowd-sourcing strategy, in which a human manually verifies the crawled sentences, to reduce the cost of data collection and annotation. Manually annotating a large number of sentences with multiple semantic categories (i.e thousands of concepts and tags) has two major shortcom-ings, (a) manual annotations are error-prone because a human cannot be attentive to every detail occurring in the context that leads to mislabeling and are difficult to eradicate; (b) large scale sentence annotation in specific is very time consuming task due to the amount and various sort of tags.

To overcome these issues, we employ a two-stage framework for the LSCP annotation. In the first stage, we utilize the StanfordNLP [START_REF] Qi | Universal dependency parsing from scratch[END_REF] to get rough annotations of the sentences. The model predicts tags per tweet. Annotations consist lists of sentences and words, base forms of those words, their parts of speech, and a syntactic structure dependency.

For translation, we utilized Google Cloud Translation 1 to provide translations. Then, generated sentences are compared to the output of the combination of Moses [START_REF] Koehn | Moses: Open source toolkit for statistical machine translation[END_REF] and Transformer [START_REF] Vaswani | Attention is all you need[END_REF] model implemented with [START_REF] Klein | Opennmt: Open-source toolkit for neural ma-chine translation[END_REF] and trainned on XNLI [START_REF] Conneau | Xnli: Evaluating cross-lingual sentence representations[END_REF] with fine-tunning on English-Persian Parallel Corpus [START_REF] Karimi | Extracting an english-persian parallel corpus from comparable corpora[END_REF].

In the second stage, we apply human verification to remove any possible mislabeled noisy tags and also add possible missing tags by the model from recommended list. They were also able to edit words or phrases in English translation for improving the overall quality of parallel sentences.

Figure 2 shows sample of the extracted dataset with annotation. Final dataset is enriched with 64M labels for words and over 130M translated sentences.

Colloquialism Temporal Network

In this section, we study the Deep Net for colloquial language understanding and then describe our new proposed "Colloquialism Temporal Network" (CATNet) for multitask word classification and prediction. This network is producing the final translation by ensembling decoding output of the translation models. The embedding layer is a matrix of shape (v + 1) where v is the vocabulary size and d is the dimension of each word vector. We set v to 10M, d to 400, and choose weights at random from (0, 0.01). As for the word embedding model, we use GloVe [START_REF] Pennington | Glove: Global vectors for word representation[END_REF]. Therefore, we feed corresponding matching words in v with its high-level annotation labels. CAT-Net enhance temporal convolution with a kernel with zero padding, relu activation function, and global max pooling over time to reduce dimensionality. Furthermore, sigmoid activation function is also used, and dropout layers are utilized to regularize the network and prevent overfitting. Using the embedding layer as in CNN models, we implement RNN models using the LSTM layers. Precisely, we implemented a random-embedding model as well as a GloVe embedding model. mented with the TensorFlow [START_REF] Abadi | Tensorflow: A system for large-scale machine learning[END_REF] and ran on a machine with GeForce RTX 2080 Ti. In the future work, we plan to encode first n-words in translation results to further improve the quality.

Conclusion

This work presents the "Large Scale Colloquial Persian Dataset" (LSCP), a large-scale multi-purpose language understanding dataset with comprehensive tasks and annotations. It contains 27M tweets in total with 120M sentences, which is richly labeled over 64M labels encompassing universal and treebank-specific POS tags with dependency relation. We believe that the LSCP dataset as an important source to learn informal Persian language representations which will enable many real-world applications. For the future plan, we are going to expand the dataset with similar rich semantic labels and also provide annotations for other important tasks.

Acknowledgements

The research was supported by the grants 19-26934X (NEUREM3) of the Czech Science Foundation. The au-

Table 1 :

 1 Dataset statistics for train, validation, and test sets.

		؟	ی کی
		VP	ADVP
	PUNC N SING	V SUB	ADV
	4	3	2	1
		ccomp	advmod
		punc
		Sentiment Polarity: 0.39
		come to your house
	HI मै ं तु म् हारे घर कब आऊं गा?
	Figure 1: A sample of the extracted sentence with depen-dency relations in syntactic annotation, part-of-speech tags, sentiment polarity and translations.

EN When will I get your blood ? CS Kdy přijdu k vám domů? DE Wann komme ich zu dir nach hause?

IT Quando verrò a casa tua?

In the frame of this research, we propose a new dataset for Large-Scale Colloquial Persian (LSCP) language understanding. The LSCP contains approx. 120M sentences from 27M tweets in total with annotations for training and validation set spanning over words and sentences. LSCP

Table 2 :

 2 Mach keinen Lärm lass mich schlafen ich habe morgen einen Flug. Non fare rumore fammi dormire domani ho un volo. HI शोर मत करो मु झे सोने दो मै ं कल एक उड़ ान है ।.Figure 2: A sample of the extracted sentence with dependency relations in syntactic annotation, part-of-speech tags, sentiment polarity and translations. Part-of-speech tags in UPC.

			.		ی		ی ک
	VP	NP	ADVP	VP	VP			VP
	PUNC V PRS	N SING	ADV TIME	V SUB	V SUB	V SUB N SING CONJ N SING
	. . 10	have 9	flight 8	tomorrow 7	sleep 6	ی let 5		ی ک don't 4	voice 3	and 2	head 1
	punc		lvc		ccomp	ccomp	posoep	nsubj
			advmod
				ccomp					conj
				Sentiment Polarity: -0.42
	EN Don't make noise let me sleep I have a flight tomorrow.
	Category	Description
	ADJ ADJ CMPR ADJ INO ADJ SUP ADJ VOC ADV ADV COMP Adverb of comparison Adjective Comparative adjective Participle adjective Superlative adjective NUM INT N PL N SING Singular noun Interjection Plural noun Numeral Vocative adjective N VOC Vocative noun Adverb P Preposition PREV Preverbal particle ADV I Adverb of interrogation PRO Pronoun ADV LOC Adverb of location SYM Symbol ADV NEG Adverb of negation V AUX Auxiliary verb ADV TIME Adverb of time V COP Verb copula CLITIC Accusative marker V IMP Imperative verb CON Conjunction V PA Past tense verb DELM Delimiter V PP Past participle verb DET Determiner V PRS Present tense verb FW Foreign Word V SUB Subjunctive verb

CS Nedělejte hluk nechte mě spát mám zítra let.

DE IT

Table 3 :

 3 Syntactic relations in UPDT.


Hadi Abdi Khojasteh

Ebrahim Ansari
email: ansari@iasbs.ac.ir

Parvin Razzaghi
email: p.razzaghi@iasbs.ac.ir

Akbar Karimi
email: akbar.karimi@unipr.it

Deep Multimodal Image-Text Embeddings for Automatic Cross-Media Retrieval

This paper considers the task of matching images and sentences by learning a visual-textual embedding space for cross-modal retrieval. Finding such a space is a challenging task since the features and representations of text and image are not comparable. In this work, we introduce an end-to-end deep multimodal convolutional-recurrent network for learning both vision and language representations simultaneously to infer image-text similarity. The model learns which pairs are a match (positive) and which ones are a mismatch (negative) using a hinge-based triplet ranking. To learn about the joint representations, we leverage our newly extracted collection of tweets from Twitter. The main characteristic of our dataset is that the images and tweets are not standardized the same as the benchmarks. Furthermore, there can be a higher semantic correlation between the pictures and tweets contrary to benchmarks in which the descriptions are well-organized. Experimental results on MS-COCO benchmark dataset show that our model outperforms certain methods presented previously and has competitive performance compared to the state-of-the-art. The code and dataset have been made available publicly.

Introduction

The advent of social networks has brought about a plethora of opportunities for everyone to share information online in the forms of text, image, video and so forth. As a result, there is a vast amount of raw data on the Net which could be helpful in dealing with many challenges in natural language processing and image recognition. Matching pictures with their textual descriptions is one of these challenges in which the research interest has been growing [START_REF] Wang | Cnn+ cnn: Convolutional decoders for image captioning[END_REF][START_REF] Eisenschtat | Linking image and text with 2-way nets[END_REF][START_REF] Faghri | Vse++: Improving visual-semantic embeddings with hard negatives[END_REF][START_REF] Lee | Stacked cross attention for image-text matching[END_REF].

The goal in image-text matching is, given an im-An African giraffe stares through the spiralshaped horns of a Greater Kudu bull in this well-timed photo by Your Shot photographer Dries Alberts https://on.natgeo.com/2J0GRNO

Ireland have spoiled the party for England England have been beaten, their 18 match winning run ended #IREvENG age, to automatically retrieve a natural language description of this image. In addition, given a caption (textual image description), we want to match it with the most related image found in our dataset as shown in Fig. 1. The process involves modeling the relationship between images and texts or captions used to describe them. This defines the semantics of a language by grounding it to the visual world.

Many studies have explored the task of crossmodal retrieval on the level of sentence and image regions [START_REF] Wang | Cnn+ cnn: Convolutional decoders for image captioning[END_REF][START_REF] Niu | Hierarchical multimodal lstm for dense visual-semantic embedding[END_REF][START_REF] Karpathy | Deep visualsemantic alignments for generating image descriptions[END_REF][START_REF] Liu | Learning a recurrent residual fusion network for multimodal matching[END_REF]. [START_REF] Karpathy | Deep fragment embeddings for bidirectional image sentence mapping[END_REF] work on matching parts of an image objects with phrases by using dependency tree relations for sentence fragments and finding a common space for representing fragments. [START_REF] Huang | Instance-aware image and sentence matching with selective multimodal lstm[END_REF] propose a sm-LSTM where they utilize a multimodal contextmodulated global attention scheme and LSTM to predict the salient instance pairs. Recently, many researchers [START_REF] Huang | Learning semantic concepts and order for image and sentence matching[END_REF][START_REF] Yan | Deep correlation for matching images and text[END_REF][START_REF] Zheng | Dual-path convolutional image-text embedding with instance loss[END_REF][START_REF] Donahue | Long-term recurrent convolutional networks for visual recognition and description[END_REF][START_REF] Lev | Rnn fisher vectors for action recognition and image annotation[END_REF][START_REF] Mao | Deep captioning with multimodal recurrent neural networks (m-rnn)[END_REF][START_REF] Gu | Look, imagine and match: Improving textual-visual cross-modal retrieval with generative models[END_REF] introduced a neural network model for im-age caption retrieval consists of RNNs, CNNs, and additional multimodal layers. Practically, one of the reasons that these deep learning approaches have been on the rise is the availability of abundant information on the Web. The next section describes the proposed model.

Model

In this work, we introduce an end-to-end multimodal neural network for learning image and text representations simultaneously. The architecture is illustrated in Fig. 2. It consists of two main subnets, a CNN for input image representation with an embedding and an LSTM to map the captions into the new space. The purpose of the model is to find a mapping from the text and image to a common space in order to represent them with similar embeddings. In this space, an image (text) will have a similar representation to its text (image) but a different one from other texts (images). Once the model is trained, by feeding an image (text) to the network, we find the most similar text (image).

Image representation

For our initial model, after removing the fullyconnected layer from ResNet-50 [START_REF] Xie | Aggregated residual transformations for deep neural networks[END_REF] which has been pre-trained on ImageNet [START_REF] Russakovsky | Imagenet large scale visual recognition challenge[END_REF], we treat the remaining layers as an image feature extractor. The inputs of the network are 224×224 images and the output is a 2048×7×7 feature vector. Therefore, a dense layer with the size of text domain is added to the end of the network. With the rest of the network, this layer which is now part of the model, is trained to produce image representations. If we call this vector I, which is a representation of the input image, then f img is a visual descriptor that is the result of forward pass in the network. The forward pass is denoted by F img (.), which is a non-linear function and is defined as f img = F img (I). Conventionally, the image model is considered as one part of our network with its pre-trained weights to avoid computing a large number of learnable parameters which is a time-consuming process. Then, we add two 1024 fully-connected layers to transform f img (.) to an image feature vector (v img ) computed by v img = W img f img (.) + b img .

Text representation

Each input text (T ) is first represented by an n×d matrix, with n being its length and d being the size of the dictionary. To build the dictionary, stop words and punctuation marks are removed and all the words are stemmed using porter stemmer. In addition, the removal of the special characters is carried out and the remaining words are all in lowercase format. Each word in the final dictionary is represented by a one-hot d dimensional vector and every word can nd an index l in the dictionary. Therefore, for an input sentence T with m words, there is a d×m matrix as the following:

T (i, j) = 1 j = li 0 otherwise
where

1 ≤ i ≤ m and 1 ≤ j ≤ d.
Based on this definition, each text should have a fixed length. In this study, since two datasets with various distributions are employed, the length of each sentence is considered a fixed number. In order to meet this criterion, when there are several sentences for one image, we concatenate all the words and build a long description for that image. When the length grows to be more than the expected length, the extra words are removed and when there are fewer words, zero-padding is applied. Therefore, we will have a 70×d dimension space for the representation of the sentences.

The input of the text representation model is a sequence of integer numbers. In the next step, a word embedding is used to reduce the number of semantically similar words or to remove the words with low frequency, which are non-existent in the dictionary, resulting in a new embedding space. Since the vocabulary size is very large, the reduction is helpful in increasing the networks generalizability. The new embeddings are then fed into an LSTM [START_REF] Gers | Learning to forget: Continual prediction with lstm[END_REF] to learn a probability distribution over the above-mentioned sequence in order to predict the next word. The output of the LSTM is not used for word-level labeling. Instead, for the representation of the whole text, only the last hidden state is utilized. Therefore, for the input sentence T, its text descriptor denoted by f txt and using the function F txt (.), is computed as f txt = F txt (T ). The final word feature vector (v txt ) is defined by f txt (.).

Alignment Objective

Having an aligned collection of image-text pairs, the goal is to learn the image-text similarity score denoted by S(T, I) which is defined as follows:

S(T, I) = -E(vtxt, vimg)
where v img and v txt are the same-size image and text representations which have been pro- jected onto a partial order visual-semantic embedding space. The penalty paid for every true pair of points that disagree is E(x, y) = ||max(0, y -x)|| 2 .

To compute the training loss, the image and text output vectors (v img , v txt ) have been forced to be in the R + . By merging the image and text embedded models as illustrated in Fig. 2, we achieve the desired visual-semantic model. To learn an order encoding function, we considered a hinge-based triplet loss function which encourages positive examples to have zero penalty, and negative examples to have penalty greater than a margin:

(T,I) ( T (max{0, α -S(T, I) + S(T , I)} -σ 2 (T )) + I (max{0, α -S(T, I) + S(T, I )})) -σ 2 (I ))

where S(T, I), the similarity score function, is as described above while T and I are inferred from the ground truth by matching contrastive images with each caption and the reverse. σ 2 (x) is discrete variance written as n

x -µc |n|

. For computational efficiency, rather than summing over all the negative samples, we assumed only the negatives in a mini-batch.

Experiments

Implementation

The proposed method has been implemented with the TensorFlow [START_REF] Abadi | Tensorflow: A system for large-scale machine learning[END_REF], and Python ran on a machine with GeForce GTX 1080 Ti. For initialization, the GloVe [START_REF] Pennington | Glove: Global vectors for word representation[END_REF]

Evaluation

Given a sentence (image), all the images (captions) of the test set are retrieved and listed based on their penalty in an increasing order. Then we report the results using Recall and Median Rank.

Recall is a metric for assessing how well a system retrieves information to a query. It is computed by dividing the number of relevant retrieved results by the total number of instances. In R@K, the top K results are treated as the output and the Recall is computed accordingly. Med r is the middle number in a sorted sequence of the retrieved instances.

To address this issue, other metrics can be taken into account since the existing measures can be intrinsically problematic [START_REF] Bernardi | Automatic description generation from images: A survey of models, datasets, and evaluameasures[END_REF]. For instance, the retrieval of the exact image (text) is not guaranteed. In these cases, since the exact matches have not been retrieved, its score is con- 1: Image and sentence retrieval results on MS-COCO. "Sentence Retrieval" denotes using an image as query to search for the relevant sentences, and "Image Retrieval" denotes using a sentence to nd the relevant image. R@K is Recall@K (high is good). Med r is the median rank (low is good).

Task Sentence Retrieval Image Retrieval Method R@1 R@5 R@10 Med r R@1 R@5 R@
sidered although similar ones have been matched.

Data Collection and Results

Several datasets have been published for imagesentence retrieval task [START_REF] Rashtchian | Collecting image annotations using amazon's mechanical turk[END_REF][START_REF] Ordonez | Im2text: Describing images using 1 million captioned photographs[END_REF][START_REF] Young | From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions[END_REF][START_REF] Hu | Twitter100k: A real-world dataset for weakly supervised cross-media retrieval[END_REF][START_REF] Farhadi | Every picture tells a story: Generating sentences from images[END_REF]. We collect a dataset, as a proof of concept, for evaluating and analyzing our method to better showcase its ability to generalize as well as for demonstrating the extensibility of this type of solution to conversational texts and unusual images. Moreover, we used MS-COCO [START_REF] Lin | Microsoft coco: Common objects in context[END_REF] to train and test the proposed model. This dataset contains 123,287 images and 616,767 descriptions [START_REF] Lin | Microsoft coco: Common objects in context[END_REF]. Each image contains 5 textual descriptions on average which collected by crowdsourcing on AMT. The average caption length is 8.7 words after rare word removal. We follow the protocol in [START_REF] Karpathy | Deep fragment embeddings for bidirectional image sentence mapping[END_REF] and use 5000 images for both validation and testing, and also report results on a subset of 1000 testing images in Table 1. We collected 13751 tweets with 14415 images by a crawler based on the Twitter API. To make sure that the collection is diverse, we first created a list of seed users. Then, the followers of the seed accounts were added to the list. Next, the latest tweets of the users in our list were extracted and saved in the dataset. To make the data appropriate for our task, we removed retweets, the tweets with no images, non-English tweets and the ones that had less than three words. This led the dataset to have a relatively long description for each image and at least one image for every tweet. At the final step, the dataset was examined by two professionals and unrelated content was removed by them. Fig. 1 shows samples of the extracted dataset. This collection is different from currently existing ones due to varied domains, informal texts and high level correlation between text and image. For instance, the tweets may contain abbreviations, initialisms, hashtags or URLs. On collected tweets, our model improves sentence retrieval by 14.3% relatively and image retrieval by 16.4% relatively based on R@1. The dataset has been available.1

Discussion

We propose a multi-modal image-text matching model using a convolutional neural network and a long short-term memory along with fullyconnected layers. They are employed to map image and text inputs into a shared feature space, where their representations can be compared, to find the closest pairs. Additionally, a new dataset of images and tweets extracted from Twitter is introduced, with the aim of having a characteris-tically different collection from the benchmarks. Whereas the descriptions in the benchmarks are well-organized, our dataset has not been standardized and the image-text pairs can contain high semantic correlations. Also, because of a varied number of domains existent in the extracted dataset, the task of image-text matching becomes even more challenging. Therefore, it can be used to carry out new research and assess the robustness of the proposed frameworks. Our experiments on MS-COCO yield improved results over some previously proposed architectures.

Figure 1 :

 1 Figure 1: Motivation/Concept Figure: Given an image (caption), the goal in image-text matching is to automatically retrieve the closest textual description (image) for that. Tweets are examples of collected dataset.

Figure 2 :

 2 Figure 2: Proposed end-to-end multimodal neural network architecture for learning the image and text representations. Image features are extracted by a CNN with 16 residual blocks and text features are extracted by recurrent unit. Then the fully-connected layers join the two domains by feature transformation.

  Turn leftover rice into a delicious new meal with this crispy rice with shrimp, bacon and corn recipe https://nyti.ms/2NyEkrQ

	Input Text
			7x7,64 Conv
	1x1,512	1x1,256	1x1,128	1x1,64
	3x3,512	3x3,256	3x3,128	3x3,64
	1x1,2048	1x1,1024	1x1,512	1x1,256
	x3 Conv	x6 Conv	x4 Conv	x3 Conv

Table

								10	Med r
	Random Ranking	0.1	0.6	1.1	631	0.1	0.5	1.0	500
	STV (2015)	33.8	67.7	82.1	3	25.9	60.0	74.6	4
	DVSA (2015)	38.4	69.9	80.5	1	27.4	60.2	74.8	3
	GMM-FV (2015)	39.0	67.0	80.3	3	24.2	59.3	76.0	4
	MM-ENS (2015) m-RNN (2014) m-CNN (2015) HM-LSTM (2017) SPE (2016) VQA-A (2016)	39.4 41.0 42.8 43.9 50.1 50.5	67.9 73.0 73.1 -79.7 80.1	80.9 83.5 84.1 87.8 89.2 89.7	2 2 2 2 --	25.1 29.0 32.6 36.1 39.6 37.0	59.8 42.2 68.6 -75.2 70.9	76.6 77.0 82.8 86.7 86.9 82.9	4 3 3 3 --	1K test images
	2WayNet (2017)	55.8	75.2	-	-	39.7	63.3	-	-
	sm-LSTM (2017)	53.2	83.1	91.5	1	40.7	75.8	87.4	2
	RRF-Net (2017)	56.4	85.3	91.5	-	43.9	78.1	88.6	-
	VSE++ (2017)	64.6	90.0	95.7	1	52.0	84.3	92.0	1
	SCAN (2018)	72.7	94.8	98.4	-	58.8	88.4	94.8	-
	Ours	47.5	81.0	91.0	2	48.4	84.3	91.5	2
	GMM-FV (2015) DVSA (2015) VQA-A (2016) VSE++ (2017) SCAN (2018) Ours	17.3 16.5 23.5 41.3 50.4 23.8	39.0 39.2 50.7 71.1 82.2 53.7	50.2 52.0 63.6 81.2 90.0 67.3	10 9 -2 -4	10.8 10.7 16.7 30.3 38.6 25.6	28.3 29.6 40.5 59.4 69.3 55.1	40.1 42.2 53.8 72.4 80.4 68.4	17 14 -4 -3	5K test images

Dataset, source codes and model will be publicly available after publishing the paper.

Acknowledgments

The research was partially supported by OP RDE project No. CZ.02.2.69/0.0/0.0/16 027/0008495, International Mobility of Researchers at Charles University. This project was in part supported by a grant from Institute for Research in Fundamental Sciences (IPM). We gratefully acknowledge the support of NVIDIA Corporation with the donation of the GPU used for this research.