The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
Minimum Risk Training for Neural Machine Translation
Open accessAbstract
We propose minimum risk training for end-to-end neural machine translation.Unlike conventional maximum likelihood estimation, minimum risk training is capable of optimizing model parameters directly with respect to arbitrary evaluation metrics, which are not necessarily differentiable.Experiments show that our approach achieves significant improvements over maximum likelihood estimation on a state-of-the-art neural machine translation system across various languages pairs.Transparent to architectures, our approach can be applied to more neural networks and potentially benefit more NLP tasks.
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Cites
Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
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Sequence to Sequence Learning with Neural Networks
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Neural Machine Translation by Jointly Learning to Align and Translate
Bahdanau, D., Cho, K., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1409.0473 · Cited by 16,213
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Effective Approaches to Attention-based Neural Machine Translation
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Statistical phrase-based translation
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Cited by
Incorporating Copying Mechanism in Sequence-to-Sequence Learning
Gu, J., Lu, Z., Li, H., & Li, V. O. K. (2016) · Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p16-1154 · Cited by 1,446
Adversarial Learning for Neural Dialogue Generation
Li, J., Monroe, W., Shi, T., Jean, S., Ritter, A., & Jurafsky, D. (2017) · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d17-1230 · Cited by 873
Sequence-Level Knowledge Distillation
Kim, Y., & Rush, A. M. (2016) · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d16-1139 · Cited by 836
Sequence-to-Sequence Learning as Beam-Search Optimization
Wiseman, S., & Rush, A. M. (2016) · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d16-1137 · Cited by 481
Improved Image Captioning via Policy Gradient optimization of SPIDEr
Liu, S., Zhu, Z., Ye, N., Guadarrama, S., & Murphy, K. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.100 · Cited by 430
“, [13, 30, 2, 16]), but mostly in the context of machine translation.”
Background
Metadata
- Authors
- Shen, S., Cheng, Y., He, Z., He, W., Wu, H., Sun, M., & Liu, Y.
- Venue
- Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Published
- 2016
- Pages
- 1683-1692
- OpenAlex
- W2963463964
- Cited by
- 375
- References
- 25
- FWCI
- 38.66
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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- Shen, S., Cheng, Y., He, Z., He, W., Wu, H., Sun, M., & Liu, Y. (2016). Minimum Risk Training for Neural Machine Translation. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). https://doi.org/10.18653/v1/p16-1159
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