@inproceedings{zhao-etal-2020-active,
    title = "Active Learning Approaches to Enhancing Neural Machine Translation",
    author = "Zhao, Yuekai  and
      Zhang, Haoran  and
      Zhou, Shuchang  and
      Zhang, Zhihua",
    editor = "Cohn, Trevor  and
      He, Yulan  and
      Liu, Yang",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2020.findings-emnlp.162/",
    doi = "10.18653/v1/2020.findings-emnlp.162",
    pages = "1796--1806",
    abstract = "Active learning is an efficient approach for mitigating data dependency when training neural machine translation (NMT) models. In this paper, we explore new training frameworks by incorporating active learning into various techniques such as transfer learning and iterative back-translation (IBT) under a limited human translation budget. We design a word frequency based acquisition function and combine it with a strong uncertainty based method. The combined method steadily outperforms all other acquisition functions in various scenarios. As far as we know, we are the first to do a large-scale study on actively training Transformer for NMT. Specifically, with a human translation budget of only 20{\%} of the original parallel corpus, we manage to surpass Transformer trained on the entire parallel corpus in three language pairs."
}