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Robust Neural Machine Translation with Joint Textual and Phonetic Embedding
Open accessAbstract
Neural machine translation (NMT) is notoriously sensitive to noises, but noises are almost inevitable in practice.One special kind of noise is the homophone noise, where words are replaced by other words with similar pronunciations.1 We propose to improve the robustness of NMT to homophone noises by 1) jointly embedding both textual and phonetic information of source sentences, and 2) augmenting the training dataset with homophone noises.Interestingly, to achieve better translation quality and more robustness, we found that most (though not all) weights should be put on the phonetic rather than textual information.Experiments show that our method not only significantly improves the robustness of NMT to homophone noises, but also surprisingly improves the translation quality on some clean test sets.
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“For 2-way classification, we compare against Thorne and Vlachos [57], who train RoBERTa [35] to classify the claim as true or false given the gold evidence sentence. RAG achieves an accuracy within 2.7% of this model, despite being supplied with only the claim and retrieving its own evidence.”
From Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks (2020) · cites this paper as [35]
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Cites
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Convolutional Sequence to Sequence Learning
Gehring, J., Auli, M., Grangier, D., Yarats, D., & Dauphin, Y. N. (2017) · arXiv · doi:10.48550/arxiv.1705.03122 · Cited by 2,850 · cited 1×
“Further improvements, such as replacing recurrent units by convolutional units (Gehring et al., 2017) or self-attention (Vaswani et al., 2017), push the boundary of NMT, especially the transformer network proposed in Vaswani et al. (2017), which achieves the state-of-the-art (SOTA) results on many…”
Background
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Cited by
Sorted by citations
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W.-T., Rocktäschel, T., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.11401 · Cited by 3,101
Metadata
- Authors
- Liu, H., Ma, M., Huang, L., Xiong, H., & He, Z.
- Affiliations
- Baidu (China) (CN)
- Oregon State University (US)
- Venue
- Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
- Published
- 2019
- Pages
- 3044-3049
- ISSN
- 0736-587X (ISSN-L)
- OpenAlex
- W2963777589
- Cited by
- 56
- References
- 2
- Citations by year
- 2026: 2 · 2025: 1 · 2024: 1 · 2023: 7 · 2022: 8 · 2021: 18 · 2020: 12 · 2019: 7
- FWCI
- 3.67
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- neural machine translation, robustness, data augmentation, noise robustness
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- Liu, H., Ma, M., Huang, L., Xiong, H., & He, Z. (2019). Robust Neural Machine Translation with Joint Textual and Phonetic Embedding. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. https://doi.org/10.18653/v1/p19-1291
