| Bloom: A 176b-parameter open-access multilingual language model T Le Scao, A Fan, C Akiki, E Pavlick, S Ilić, D Hesslow, R Castagné, ... | 1623 | 2023 |
| Improving statistical machine translation using word sense disambiguation M Carpuat, D Wu Proceedings of the 2007 joint conference on empirical methods in natural …, 2007 | 472 | 2007 |
| Word sense disambiguation vs. statistical machine translation M Carpuat, D Wu Proceedings of the 43rd Annual Meeting of the Association for Computational …, 2005 | 181 | 2005 |
| Curriculum learning for domain adaptation in neural machine translation X Zhang, P Shapiro, G Kumar, P McNamee, M Carpuat, K Duh arXiv preprint arXiv:1905.05816, 2019 | 145 | 2019 |
| Task-based evaluation of multiword expressions: a pilot study in statistical machine translation M Carpuat, M Diab Human Language Technologies: The 2010 Annual Conference of the North …, 2010 | 135 | 2010 |
| An empirical exploration of curriculum learning for neural machine translation X Zhang, G Kumar, H Khayrallah, K Murray, J Gwinnup, MJ Martindale, ... arXiv preprint arXiv:1811.00739, 2018 | 127 | 2018 |
| Multi-task neural models for translating between styles within and across languages X Niu, S Rao, M Carpuat arXiv preprint arXiv:1806.04357, 2018 | 92 | 2018 |
| One translation per discourse M Carpuat Proceedings of the Workshop on Semantic Evaluations: Recent Achievements and …, 2009 | 87 | 2009 |
| How phrase sense disambiguation outperforms word sense disambiguation for statistical machine translation M Carpuat, D Wu Proceedings of the 11th Conference on Theoretical and Methodological Issues …, 2007 | 87 | 2007 |
| A study of style in machine translation: Controlling the formality of machine translation output X Niu, M Martindale, M Carpuat Proceedings of the 2017 Conference on Empirical Methods in Natural Language …, 2017 | 82 | 2017 |
| SemEval-2016 Task~ 10: Detecting Minimal Semantic Units and their Meanings (DiMSUM) N Schneider, D Hovy, A Johannsen, M Carpuat 10th International Workshop on Semantic Evaluation, 546-559, 2016 | 76 | 2016 |
| Controlling text complexity in neural machine translation S Agrawal, M Carpuat arXiv preprint arXiv:1911.00835, 2019 | 69 | 2019 |
| The trouble with SMT consistency M Carpuat, M Simard Proceedings of the Seventh Workshop on Statistical Machine Translation, 442-449, 2012 | 67 | 2012 |
| A stacked, voted, stacked model for named entity recognition D Wu, G Ngai, M Carpuat Proceedings of the seventh conference on Natural language learning at HLT …, 2003 | 66 | 2003 |
| The NRC system for discriminating similar languages C Goutte, S Léger, M Carpuat Proceedings of the first workshop on applying NLP tools to similar languages …, 2014 | 65 | 2014 |
| Measuring machine translation errors in new domains A Irvine, J Morgan, M Carpuat, H Daumé III, D Munteanu Transactions of the Association for Computational Linguistics 1, 429-440, 2013 | 65 | 2013 |
| Bi-directional neural machine translation with synthetic parallel data X Niu, M Denkowski, M Carpuat arXiv preprint arXiv:1805.11213, 2018 | 62 | 2018 |
| Assessing the discourse factors that influence the quality of machine translation JJ Li, M Carpuat, A Nenkova Proceedings of the 52nd Annual Meeting of the Association for Computational …, 2014 | 60 | 2014 |
| Boosting for named entity recognition D Wu, G Ngai, M Carpuat, J Larsen, Y Yang COLING-02: The 6th Conference on Natural Language Learning 2002 (CoNLL-2002), 2002 | 60 | 2002 |
| Fluency over adequacy: A pilot study in measuring user trust in imperfect MT MJ Martindale, M Carpuat arXiv preprint arXiv:1802.06041, 2018 | 51 | 2018 |
