@inproceedings{park-etal-2021-unsupervised,
title = "Unsupervised Neural Machine Translation for Low-Resource Domains via Meta-Learning",
author = "Park, Cheonbok and
Tae, Yunwon and
Kim, TaeHee and
Yang, Soyoung and
Khan, Mohammad Azam and
Park, Lucy and
Choo, Jaegul",
editor = "Zong, Chengqing and
Xia, Fei and
Li, Wenjie and
Navigli, Roberto",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.225",
doi = "10.18653/v1/2021.acl-long.225",
pages = "2888--2901",
abstract = "Unsupervised machine translation, which utilizes unpaired monolingual corpora as training data, has achieved comparable performance against supervised machine translation. However, it still suffers from data-scarce domains. To address this issue, this paper presents a novel meta-learning algorithm for unsupervised neural machine translation (UNMT) that trains the model to adapt to another domain by utilizing only a small amount of training data. We assume that domain-general knowledge is a significant factor in handling data-scarce domains. Hence, we extend the meta-learning algorithm, which utilizes knowledge learned from high-resource domains, to boost the performance of low-resource UNMT. Our model surpasses a transfer learning-based approach by up to 2-3 BLEU scores. Extensive experimental results show that our proposed algorithm is pertinent for fast adaptation and consistently outperforms other baselines.",
}
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<abstract>Unsupervised machine translation, which utilizes unpaired monolingual corpora as training data, has achieved comparable performance against supervised machine translation. However, it still suffers from data-scarce domains. To address this issue, this paper presents a novel meta-learning algorithm for unsupervised neural machine translation (UNMT) that trains the model to adapt to another domain by utilizing only a small amount of training data. We assume that domain-general knowledge is a significant factor in handling data-scarce domains. Hence, we extend the meta-learning algorithm, which utilizes knowledge learned from high-resource domains, to boost the performance of low-resource UNMT. Our model surpasses a transfer learning-based approach by up to 2-3 BLEU scores. Extensive experimental results show that our proposed algorithm is pertinent for fast adaptation and consistently outperforms other baselines.</abstract>
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%0 Conference Proceedings
%T Unsupervised Neural Machine Translation for Low-Resource Domains via Meta-Learning
%A Park, Cheonbok
%A Tae, Yunwon
%A Kim, TaeHee
%A Yang, Soyoung
%A Khan, Mohammad Azam
%A Park, Lucy
%A Choo, Jaegul
%Y Zong, Chengqing
%Y Xia, Fei
%Y Li, Wenjie
%Y Navigli, Roberto
%S Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F park-etal-2021-unsupervised
%X Unsupervised machine translation, which utilizes unpaired monolingual corpora as training data, has achieved comparable performance against supervised machine translation. However, it still suffers from data-scarce domains. To address this issue, this paper presents a novel meta-learning algorithm for unsupervised neural machine translation (UNMT) that trains the model to adapt to another domain by utilizing only a small amount of training data. We assume that domain-general knowledge is a significant factor in handling data-scarce domains. Hence, we extend the meta-learning algorithm, which utilizes knowledge learned from high-resource domains, to boost the performance of low-resource UNMT. Our model surpasses a transfer learning-based approach by up to 2-3 BLEU scores. Extensive experimental results show that our proposed algorithm is pertinent for fast adaptation and consistently outperforms other baselines.
%R 10.18653/v1/2021.acl-long.225
%U https://aclanthology.org/2021.acl-long.225
%U https://doi.org/10.18653/v1/2021.acl-long.225
%P 2888-2901
Markdown (Informal)
[Unsupervised Neural Machine Translation for Low-Resource Domains via Meta-Learning](https://aclanthology.org/2021.acl-long.225) (Park et al., ACL-IJCNLP 2021)
ACL
- Cheonbok Park, Yunwon Tae, TaeHee Kim, Soyoung Yang, Mohammad Azam Khan, Lucy Park, and Jaegul Choo. 2021. Unsupervised Neural Machine Translation for Low-Resource Domains via Meta-Learning. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 2888–2901, Online. Association for Computational Linguistics.