On NMT Search Errors and Model Errors: Cat Got Your Tongue?
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Felix Stahlberg, Bill Byrne. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.
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Similar issues of repetition and genericness have been reported when using large beam sizes and with exact inference.
“Still, similar issues have been reported when using large beam sizes (Koehn & Knowles, 2017) and more recently with exact inference (Stahlberg & Byrne, 2019), a counter-intuitive observation since more comprehensive search helps maximize probability.”
From The Curious Case of Neural Text Degeneration (2019) · cites this paper as Stahlberg & Byrne, 2019
The passages available from our copy are bibliographic records without paper sections or page text. Searches for exact inference, repetition, genericness, and beam-search errors returned no passage addressing the reported issues, so the attribution cannot be verified from our copy. (LLM, conf=0.72)
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Cites
Correcting Length Bias in Neural Machine Translation
Murray, K., & Chiang, D. (2018) · Proceedings of the Third Conference on Machine Translation: Research Papers · doi:10.18653/v1/w18-6322 · Cited by 133 · cited 2× · see citing works
“A similar argument was made by Murray and Chiang (2018) who pointed out the difficulty for a locally normalized model to estimate the “budget” for all remaining (longer) translations.”
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Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al. (2025) · ACM Transactions on Information Systems · doi:10.1145/3703155 · Cited by 2,142 · see citing works
“The rationale for incorporating randomness into decoding strategies stems from the realization that high likelihood sequences often result in surprisingly low-quality text, which is called likelihood trap (Stahlberg and Byrne, 2019; Holtzman et al., 2020; Meister et al., 2020; Zhang et al., 2021).”
The Curious Case of Neural Text Degeneration
Holtzman, A., Buys, J., Du, L., Forbes, M., & Choi, Y. (2019) · arXiv · doi:10.48550/arxiv.1904.09751 · Cited by 1,304 · see citing works
Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al. (2023) · arXiv · doi:10.48550/arxiv.2311.05232 · Cited by 2,516 · see citing works
“The rationale for incorporating randomness into decoding strategies stems from the realization that high likelihood sequences often result in surprisingly low-quality text, which is called likelihood trap (Stahlberg and Byrne, 2019; Holtzman et al., 2020; Meister et al., 2020; Zhang et al., 2021).”
On Exposure Bias, Hallucination and Domain Shift in Neural Machine Translation
Wang, C., & Sennrich, R. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.326 · Cited by 118 · see citing works
Self-Evaluation Guided Beam Search for Reasoning
Xie, Y., Kawaguchi, K., Zhao, Y., Zhao, J. X., Kan, M.-Y., He, J., & Xie, M. (2023) · Advances in Neural Information Processing Systems 36 · doi:10.52202/075280-1802 · Cited by 27
- Authors
- Stahlberg, F., & Byrne, B.
- Affiliations
- University of Cambridge (GB)
- Venue
- Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
- Published
- 2019
- Pages
- 3354-3360
- OpenAlex
- W2970692082
- Cited by
- 107
- References
- 1
- Citations by year
- 2026: 2 · 2025: 2 · 2024: 6 · 2023: 23 · 2022: 26 · 2021: 25 · 2020: 20 · 2019: 3
- FWCI
- 7.08
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference on Empirical Methods in Natural Language Processing (EMNLP) (publishedVersion, cc-by)
- Funders
- University of Cambridge (EP/P020259/1)
- Engineering and Physical Sciences Research Council (EP/L027623/1, EP/P020259/1, EP/P020259, EPSRC Tier-2 capital grant EP/P020259/1)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Keywords
- neural machine translation, search errors, model error, translation quality, error analysis
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