Abstract
Word sense disambiguation is a well-known source of translation errors in NMT. We posit that some of the incorrect disambiguation choices are due to models’ over-reliance on dataset artifacts found in training data, specifically superficial word co-occurrences, rather than a deeper understanding of the source text. We introduce a method for the prediction of disambiguation errors based on statistical data properties, demonstrating its effectiveness across several domains and model types. Moreover, we develop a simple adversarial attack strategy that minimally perturbs sentences in order to elicit disambiguation errors to further probe the robustness of translation models. Our findings indicate that disambiguation robustness varies substantially between domains and that different models trained on the same data are vulnerable to different attacks.- Anthology ID:
- 2020.emnlp-main.616
- Volume:
- Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Bonnie Webber, Trevor Cohn, Yulan He, Yang Liu
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 7635–7653
- Language:
- URL:
- https://aclanthology.org/2020.emnlp-main.616
- DOI:
- 10.18653/v1/2020.emnlp-main.616
- Cite (ACL):
- Denis Emelin, Ivan Titov, and Rico Sennrich. 2020. Detecting Word Sense Disambiguation Biases in Machine Translation for Model-Agnostic Adversarial Attacks. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 7635–7653, Online. Association for Computational Linguistics.
- Cite (Informal):
- Detecting Word Sense Disambiguation Biases in Machine Translation for Model-Agnostic Adversarial Attacks (Emelin et al., EMNLP 2020)
- PDF:
- https://preview.aclanthology.org/add_acl24_videos/2020.emnlp-main.616.pdf
- Code
- demelin/detecting_wsd_biases_for_nmt