Contrastive Conditioning for Assessing Disambiguation in MT: A Case Study of Distilled Bias

Jannis Vamvas, Rico Sennrich


Abstract
Lexical disambiguation is a major challenge for machine translation systems, especially if some senses of a word are trained less often than others. Identifying patterns of overgeneralization requires evaluation methods that are both reliable and scalable. We propose contrastive conditioning as a reference-free black-box method for detecting disambiguation errors. Specifically, we score the quality of a translation by conditioning on variants of the source that provide contrastive disambiguation cues. After validating our method, we apply it in a case study to perform a targeted evaluation of sequence-level knowledge distillation. By probing word sense disambiguation and translation of gendered occupation names, we show that distillation-trained models tend to overgeneralize more than other models with a comparable BLEU score. Contrastive conditioning thus highlights a side effect of distillation that is not fully captured by standard evaluation metrics. Code and data to reproduce our findings are publicly available.
Anthology ID:
2021.emnlp-main.803
Volume:
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
Month:
November
Year:
2021
Address:
Online and Punta Cana, Dominican Republic
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
10246–10265
Language:
URL:
https://aclanthology.org/2021.emnlp-main.803
DOI:
10.18653/v1/2021.emnlp-main.803
Bibkey:
Cite (ACL):
Jannis Vamvas and Rico Sennrich. 2021. Contrastive Conditioning for Assessing Disambiguation in MT: A Case Study of Distilled Bias. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 10246–10265, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
Contrastive Conditioning for Assessing Disambiguation in MT: A Case Study of Distilled Bias (Vamvas & Sennrich, EMNLP 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.emnlp-main.803.pdf
Video:
 https://aclanthology.org/2021.emnlp-main.803.mp4
Code
 zurichnlp/contrastive-conditioning