The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
Transfer Learning for Low-Resource Neural Machine Translation
Open accessAbstract
The encoder-decoder framework for neural machine translation (NMT) has been shown effective in large data scenarios, but is much less effective for low-resource languages.We present a transfer learning method that significantly improves BLEU scores across a range of low-resource languages.Our key idea is to first train a high-resource language pair (the parent model), then transfer some of the learned parameters to the low-resource pair (the child model) to initialize and constrain training.Using our transfer learning method we improve baseline NMT models by an average of 5.6 BLEU on four low-resource language pairs.Ensembling and unknown word replacement add another 2 BLEU which brings the NMT performance on low-resource machine translation close to a strong syntax based machine translation (SBMT) system, exceeding its performance on one language pair.Additionally, using the transfer learning model for re-scoring, we can improve the SBMT system by an average of 1.3 BLEU, improving the state-of-the-art on low-resource machine translation.
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Cites
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Cited by
Transfer Learning in Natural Language Processing
Ruder, S., Peters, M. E., Swayamdipta, S., & Wolf, T. (2019) · Proceedings of the 2019 Conference of the North · doi:10.18653/v1/n19-5004 · Cited by 587
Efficient Neural Architecture Search via Parameter Sharing
Pham, H., Guan, M. Y., Zoph, B., Le, Q. V., & Dean, J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.03268 · Cited by 517
Self-citationA research agenda for augmented and virtual reality in architecture, engineering and construction
Davila Delgado, J. M., Oyedele, L., Demian, P., & Beach, T. (2020) · Advanced Engineering Informatics · doi:10.1016/j.aei.2020.101122 · Cited by 486
The Natural Language Decathlon: Multitask Learning as Question Answering
McCann, B., Keskar, N. S., Xiong, C., & Socher, R. (2018) · arXiv · doi:10.48550/arxiv.1806.08730 · Cited by 448
Massively Multilingual Neural Machine Translation
Aharoni, R., Johnson, M., & Firat, O. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1388 · Cited by 402
Metadata
- Authors
- Zoph, B., Yuret, D., May, J., & Knight, K.
- Venue
- Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing
- Published
- 2016
- Pages
- 1568-1575
- OpenAlex
- W2337363174
- Cited by
- 788
- References
- 19
- FWCI
- 41.93
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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- Zoph, B., Yuret, D., May, J., & Knight, K. (2016). Transfer Learning for Low-Resource Neural Machine Translation. Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. https://doi.org/10.18653/v1/d16-1163
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