| 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 | 1845 | 2017 |
| Very deep convolutional networks for text classification A Conneau, H Schwenk, L Barrault, Y Lecun arXiv preprint arXiv:1606.01781, 2016 | 977 | 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 | 609 | 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 | 478 | 2015 |
| Findings of the 2019 conference on machine translation (WMT19) L Barrault, O Bojar, MR Costa-Jussa, C Federmann, M Fishel, Y Graham Association for Computational Linguistics (ACL), 2019 | 464 | 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 | 174 | 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 | 155 | 2018 |
| 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 | 110 | 2018 |
| Probing the need for visual context in multimodal machine translation O Caglayan, P Madhyastha, L Specia, L Barrault arXiv preprint arXiv:1903.08678, 2019 | 92 | 2019 |
| 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 | 92 | 2017 |
| The MateCat tool. M Federico, N Bertoldi, M Cettolo, M Negri, M Turchi, M Trombetti, ... COLING (Demos), 129-132, 2014 | 88 | 2014 |
| The IWSLT 2019 evaluation campaign N Jan, R Cattoni, S Sebastian, M Negri, M Turchi, S Elizabeth, S Ramon, ... 16th International Workshop on Spoken Language Translation 2019, 2019 | 84 | 2019 |
| 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 | 75 | 2016 |
| Nmtpy: A flexible toolkit for advanced neural machine translation systems O Caglayan, M García-Martínez, A Bardet, W Aransa, F Bougares, ... The Prague Bulletin of Mathematical Linguistics 109 (1), 15, 2017 | 71 | 2017 |
| Multimodal attention for neural machine translation O Caglayan, L Barrault, F Bougares arXiv preprint arXiv:1609.03976, 2016 | 56 | 2016 |
| MANY: Open source machine translation system combination L Barrault The Prague Bulletin of Mathematical Linguistics 93, 147, 2010 | 41 | 2010 |
| Factored neural machine translation architectures M Garcia-Martinez, L Barrault, F Bougares International Workshop on Spoken Language Translation (IWSLT'16), 2016 | 40 | 2016 |
| 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 | 34 | 2017 |
| Findings of the IWSLT 2022 Evaluation Campaign A Anastasopoulos, L Barrault, L Bentivogli, MZ Boito, O Bojar, R Cattoni, ... Proceedings of the 19th International Conference on Spoken Language …, 2022 | 32 | 2022 |
| Translation model adaptation by resampling K Shah, L Barrault, H Schwenk Proceedings of the Joint Fifth Workshop on Statistical Machine Translation …, 2010 | 29 | 2010 |
Holger SchwenkResearch scientist, Facebook, and professor of Computer ScienceVerified email at fb.com
