Adversarial Training on Disentangling Meaning and Language Representations for Unsupervised Quality Estimation

Yuto Kuroda; Tomoyuki Kajiwara; Yuki Arase†; Takashi Ninomiya

Adversarial Training on Disentangling Meaning and Language Representations for Unsupervised Quality Estimation

Yuto Kuroda, Tomoyuki Kajiwara, Yuki Arase, Takashi Ninomiya

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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)
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PDF:: https://aclanthology.org/2022.coling-1.465.pdf

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@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

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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.