| Supervised learning of universal sentence representations from natural language inference data A Conneau, D Kiela, H Schwenk, L Barrault, A Bordes arXiv preprint arXiv:1705.02364, 2017 | 1357 | 2017 |
| Very deep convolutional networks for text classification A Conneau, H Schwenk, L Barrault, Y Lecun arXiv preprint arXiv:1606.01781, 2016 | 994* | 2016 |
| What you can cram into a single vector: Probing sentence embeddings for linguistic properties A Conneau, G Kruszewski, G Lample, L Barrault, M Baroni arXiv preprint arXiv:1805.01070, 2018 | 382 | 2018 |
| On using monolingual corpora in neural machine translation C Gulcehre, O Firat, K Xu, K Cho, L Barrault, HC Lin, F Bougares, ... arXiv preprint arXiv:1503.03535, 2015 | 351 | 2015 |
| Findings of the 2019 conference on machine translation (wmt19) L Barrault, O Bojar, MR Costa-Jussa, C Federmann, M Fishel, Y Graham, ... Proceedings of the Fourth Conference on Machine Translation (Volume 2 …, 2019 | 233 | 2019 |
| Findings of the second shared task on multimodal machine translation and multilingual image description D Elliott, S Frank, L Barrault, F Bougares, L Specia arXiv preprint arXiv:1710.07177, 2017 | 113 | 2017 |
| How2: a large-scale dataset for multimodal language understanding R Sanabria, O Caglayan, S Palaskar, D Elliott, L Barrault, L Specia, ... arXiv preprint arXiv:1811.00347, 2018 | 87 | 2018 |
| The Matecat Tool. M Federico, N Bertoldi, M Cettolo, M Negri, M Turchi, M Trombetti, ... COLING (Demos), 129-132, 2014 | 80 | 2014 |
| LIUM-CVC submissions for WMT17 multimodal translation task O Caglayan, W Aransa, A Bardet, M García-Martínez, F Bougares, ... arXiv preprint arXiv:1707.04481, 2017 | 71 | 2017 |
| Findings of the third shared task on multimodal machine translation L Barrault, F Bougares, L Specia, C Lala, D Elliott, S Frank THIRD CONFERENCE ON MACHINE TRANSLATION (WMT18) 2, 308-327, 2018 | 66 | 2018 |
| Does multimodality help human and machine for translation and image captioning? O Caglayan, W Aransa, Y Wang, M Masana, M García-Martínez, ... arXiv preprint arXiv:1605.09186, 2016 | 60 | 2016 |
| Nmtpy: A flexible toolkit for advanced neural machine translation systems O Caglayan, M García-Martínez, A Bardet, W Aransa, F Bougares, ... arXiv preprint arXiv:1706.00457, 2017 | 56 | 2017 |
| Factored neural machine translation architectures M García-Martínez, L Barrault, F Bougares International Workshop on Spoken Language Translation (IWSLT'16), 2016 | 53* | 2016 |
| Probing the need for visual context in multimodal machine translation O Caglayan, P Madhyastha, L Specia, L Barrault arXiv preprint arXiv:1903.08678, 2019 | 48 | 2019 |
| Multimodal attention for neural machine translation O Caglayan, L Barrault, F Bougares arXiv preprint arXiv:1609.03976, 2016 | 42 | 2016 |
| MANY: Open source machine translation system combination L Barrault The Prague Bulletin of Mathematical Linguistics 93, 147, 2010 | 41 | 2010 |
| A general framework to weight heterogeneous parallel data for model adaptation in statistical machine translation K Shah, L Barrault, H Schwenk, F Le Mans MT Summit, Octobre, 2012 | 34 | 2012 |
| SMT and SPE machine translation systems for WMT'09 H Schwenk, S Abdul-Rauf, L Barrault, J Senellart Fourth Workshop on Statistical Machine Translation, 130--134, 2009 | 27 | 2009 |
| Translation model adaptation by resampling K Shah, L Barrault, H Schwenk Proceedings of the Joint Fifth Workshop on Statistical Machine Translation …, 2010 | 23 | 2010 |
| Introduction to the special issue on deep learning approaches for machine translation MR Costa-jussà, A Allauzen, L Barrault, K Cho, H Schwenk Computer Speech & Language 46, 367-373, 2017 | 22 | 2017 |
Fethi bougaresAssociate Professor, University of Le MansVerified email at univ-lemans.fr
