Adversarial Training on Disentangling Meaning and Language Representations for Unsupervised Quality Estimation
Yuto Kuroda, Tomoyuki Kajiwara, Yuki Arase, Takashi Ninomiya
Abstract
We propose a method to distill language-agnostic meaning embeddings from multilingual sentence encoders for unsupervised quality estimation of machine translation. Our method facilitates that the meaning embeddings focus on semantics by adversarial training that attempts to eliminate language-specific information. Experimental results on unsupervised quality estimation reveal that our method achieved higher correlations with human evaluations.- Anthology ID:
- 2022.coling-1.465
- Volume:
- Proceedings of the 29th International Conference on Computational Linguistics
- Month:
- October
- Year:
- 2022
- Address:
- Gyeongju, Republic of Korea
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 5240–5245
- Language:
- URL:
- https://aclanthology.org/2022.coling-1.465
- DOI:
- Cite (ACL):
- Yuto Kuroda, Tomoyuki Kajiwara, Yuki Arase, and Takashi Ninomiya. 2022. Adversarial Training on Disentangling Meaning and Language Representations for Unsupervised Quality Estimation. In Proceedings of the 29th International Conference on Computational Linguistics, pages 5240–5245, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
- Cite (Informal):
- Adversarial Training on Disentangling Meaning and Language Representations for Unsupervised Quality Estimation (Kuroda et al., COLING 2022)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2022.coling-1.465.pdf