The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Achieving Open Vocabulary Neural Machine Translation with Hybrid Word-Character Models
Open accessAbstract
Nearly all previous work on neural machine translation (NMT) has used quite restricted vocabularies, perhaps with a subsequent method to patch in unknown words.This paper presents a novel wordcharacter solution to achieving open vocabulary NMT.We build hybrid systems that translate mostly at the word level and consult the character components for rare words.Our character-level recurrent neural networks compute source word representations and recover unknown target words when needed.The twofold advantage of such a hybrid approach is that it is much faster and easier to train than character-based ones; at the same time, it never produces unknown words as in the case of word-based models.On the WMT'15 English to Czech translation task, this hybrid approach offers an addition boost of +2.1-11.4BLEU points over models that already handle unknown words.Our best system achieves a new state-of-the-art result with 20.7 BLEU score.We demonstrate that our character models can successfully learn to not only generate well-formed words for Czech, a highly-inflected language with a very complex vocabulary, but also build correct representations for English source words.
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Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
BLEU: a method for automatic evaluation of machine translation
Papineni, K., Roukos, S., Ward, T., & Zhu, W.-J. (2001) · Proceedings of the 40th Annual Meeting on Association for Computational Linguistics - ACL '02 · doi:10.3115/1073083.1073135 · Cited by 21,344
Effective Approaches to Attention-based Neural Machine Translation
Luong, T., Pham, H., & Manning, C. D. (2015) · Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d15-1166 · Cited by 8,382
Self-citationCharacter-Aware Neural Language Models
Kim, Y., Jernite, Y., Sontag, D., & Rush, A. M. (2015) · arXiv · doi:10.48550/arxiv.1508.06615 · Cited by 1,530
chrF: character n-gram F-score for automatic MT evaluation
Popović, M. (2015) · Proceedings of the Tenth Workshop on Statistical Machine Translation · doi:10.18653/v1/w15-3049 · Cited by 1,220
Cited by
Enriching Word Vectors with Subword Information
Bojanowski, P., Grave, E., Joulin, A., & Mikolov, T. (2017) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00051 · Cited by 9,645
What do Neural Machine Translation Models Learn about Morphology?
Belinkov, Y., Durrani, N., Dalvi, F., Sajjad, H., & Glass, J. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p17-1080 · Cited by 307
Massively Multilingual Neural Machine Translation in the Wild: Findings and Challenges
Arivazhagan, N., Bapna, A., Firat, O., Lepikhin, D., Johnson, M., Krikun, M., Chen, M. X., Cao, Y., Foster, G., Cherry, C., et al. (2019) · arXiv · doi:10.48550/arxiv.1907.05019 · Cited by 279
When Code Completion Fails: A Case Study on Real-World Completions
Hellendoorn, V. J., Proksch, S., Gall, H. C., & Bacchelli, A. (2019) · 2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE) · doi:10.1109/icse.2019.00101 · Cited by 76
Latent Relation Language Models
Hayashi, H., Hu, Z., Xiong, C., & Neubig, G. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i05.6298 · Cited by 44
Metadata
- Authors
- Luong, M.-T., & Manning, C. D.
- Venue
- Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Published
- 2016
- Pages
- 1054-1063
- OpenAlex
- W2339995566
- Cited by
- 345
- References
- 5
- FWCI
- 40.12
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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