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
- Editors:
- Nicoletta Calzolari, Chu-Ren Huang, Hansaem Kim, James Pustejovsky, Leo Wanner, Key-Sun Choi, Pum-Mo Ryu, Hsin-Hsi Chen, Lucia Donatelli, Heng Ji, Sadao Kurohashi, Patrizia Paggio, Nianwen Xue, Seokhwan Kim, Younggyun Hahm, Zhong He, Tony Kyungil Lee, Enrico Santus, Francis Bond, Seung-Hoon Na
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 5240–5245
- Language:
- URL:
- https://aclanthology.org/2022.coling-1.465
- DOI:
- Bibkey:
- 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)
- Copy Citation:
- PDF:
- https://aclanthology.org/2022.coling-1.465.pdf
Export citation
@inproceedings{kuroda-etal-2022-adversarial,
title = "Adversarial Training on Disentangling Meaning and Language Representations for Unsupervised Quality Estimation",
author = "Kuroda, Yuto and
Kajiwara, Tomoyuki and
Arase, Yuki and
Ninomiya, Takashi",
editor = "Calzolari, Nicoletta and
Huang, Chu-Ren and
Kim, Hansaem and
Pustejovsky, James and
Wanner, Leo and
Choi, Key-Sun and
Ryu, Pum-Mo and
Chen, Hsin-Hsi and
Donatelli, Lucia and
Ji, Heng and
Kurohashi, Sadao and
Paggio, Patrizia and
Xue, Nianwen and
Kim, Seokhwan and
Hahm, Younggyun and
He, Zhong and
Lee, Tony Kyungil and
Santus, Enrico and
Bond, Francis and
Na, Seung-Hoon",
booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2022.coling-1.465",
pages = "5240--5245",
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.",
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%0 Conference Proceedings %T Adversarial Training on Disentangling Meaning and Language Representations for Unsupervised Quality Estimation %A Kuroda, Yuto %A Kajiwara, Tomoyuki %A Arase, Yuki %A Ninomiya, Takashi %Y Calzolari, Nicoletta %Y Huang, Chu-Ren %Y Kim, Hansaem %Y Pustejovsky, James %Y Wanner, Leo %Y Choi, Key-Sun %Y Ryu, Pum-Mo %Y Chen, Hsin-Hsi %Y Donatelli, Lucia %Y Ji, Heng %Y Kurohashi, Sadao %Y Paggio, Patrizia %Y Xue, Nianwen %Y Kim, Seokhwan %Y Hahm, Younggyun %Y He, Zhong %Y Lee, Tony Kyungil %Y Santus, Enrico %Y Bond, Francis %Y Na, Seung-Hoon %S Proceedings of the 29th International Conference on Computational Linguistics %D 2022 %8 October %I International Committee on Computational Linguistics %C Gyeongju, Republic of Korea %F kuroda-etal-2022-adversarial %X 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. %U https://aclanthology.org/2022.coling-1.465 %P 5240-5245
Markdown (Informal)
[Adversarial Training on Disentangling Meaning and Language Representations for Unsupervised Quality Estimation](https://aclanthology.org/2022.coling-1.465) (Kuroda et al., COLING 2022)
- Adversarial Training on Disentangling Meaning and Language Representations for Unsupervised Quality Estimation (Kuroda et al., COLING 2022)
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.