| Learning phrase representations using RNN encoder-decoder for statistical machine translation K Cho, B Van Merriënboer, C Gulcehre, D Bahdanau, F Bougares, ... arXiv preprint arXiv:1406.1078, 2014 | 19519 | 2014 |
| 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 | 1800 | 2017 |
| Very deep convolutional networks for text classification A Conneau, H Schwenk, L Barrault, Y Lecun arXiv preprint arXiv:1606.01781, 2016 | 959 | 2016 |
| XNLI: Evaluating cross-lingual sentence representations A Conneau, G Lample, R Rinott, A Williams, SR Bowman, H Schwenk, ... arXiv preprint arXiv:1809.05053, 2018 | 653 | 2018 |
| Continuous space language models H Schwenk Computer Speech & Language 21 (3), 492-518, 2007 | 621 | 2007 |
| Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv 2014 K Cho, B Van Merriënboer, C Gulcehre, D Bahdanau, F Bougares, ... arXiv preprint arXiv:1406.1078, 2020 | 588 | 2020 |
| Massively multilingual sentence embeddings for zero-shot cross-lingual transfer and beyond M Artetxe, H Schwenk Transactions of the Association for Computational Linguistics 7, 597-610, 2019 | 537 | 2019 |
| 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 | 467 | 2015 |
| Boosting neural networks H Schwenk, Y Bengio Neural computation 12 (8), 1869-1887, 2000 | 377 | 2000 |
| Very deep convolutional networks for natural language processing A Conneau, H Schwenk, L Barrault, Y Lecun arXiv preprint arXiv:1606.01781 2 (1), 2016 | 345 | 2016 |
| MLQA: Evaluating cross-lingual extractive question answering P Lewis, B Oğuz, R Rinott, S Riedel, H Schwenk arXiv preprint arXiv:1910.07475, 2019 | 211 | 2019 |
| Language recognition using phone latices. JL Gauvain, A Messaoudi, H Schwenk Interspeech, 2004 | 186 | 2004 |
| Connectionist language modeling for large vocabulary continuous speech recognition H Schwenk, JL Gauvain 2002 IEEE International Conference on Acoustics, Speech, and Signal …, 2002 | 172 | 2002 |
| Training neural network language models on very large corpora H Schwenk, JL Gauvain Proceedings of human language technology conference and conference on …, 2005 | 169 | 2005 |
| Wikimatrix: Mining 135m parallel sentences in 1620 language pairs from wikipedia H Schwenk, V Chaudhary, S Sun, H Gong, F Guzmán arXiv preprint arXiv:1907.05791, 2019 | 159 | 2019 |
| Continuous space translation models for phrase-based statistical machine translation H Schwenk Proceedings of COLING 2012: Posters, 1071-1080, 2012 | 156 | 2012 |
| Beyond English-Centric Multilingual Machine Translation. A Fan, S Bhosale, H Schwenk, Z Ma, A El-Kishky, S Goyal, M Baines, ... J. Mach. Learn. Res. 22 (107), 1-48, 2021 | 149 | 2021 |
| On the use of comparable corpora to improve SMT performance S Abdul-Rauf, H Schwenk Proceedings of the 12th Conference of the European Chapter of the ACL (EACL …, 2009 | 141 | 2009 |
| Large, pruned or continuous space language models on a gpu for statistical machine translation H Schwenk, A Rousseau, M Attik Proceedings of the NAACL-HLT 2012 Workshop: Will We Ever Really Replace the …, 2012 | 134 | 2012 |
| Margin-based parallel corpus mining with multilingual sentence embeddings M Artetxe, H Schwenk arXiv preprint arXiv:1811.01136, 2018 | 129 | 2018 |
