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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Achieving Open Vocabulary Neural Machine Translation with Hybrid Word-Character Models. https://app.cochat.ai/papers/10.18653/v1/p16-1100
- The paper
- Luong, M.-T., & Manning, C. D. (2016). Achieving Open Vocabulary Neural Machine Translation with Hybrid Word-Character Models. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). https://doi.org/10.18653/v1/p16-1100
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
