Unsupervised Neural Machine Translation for Low-Resource Domains via Meta-Learning
Cheonbok Park, Yunwon Tae, TaeHee Kim, Soyoung Yang, Mohammad Azam Khan, Lucy Park, Jaegul Choo
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.
BibTeX
@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"
}