@inproceedings{kumari-etal-2021-domain,
    title = "Domain Adaptation for {NMT} via Filtered Iterative Back-Translation",
    author = "Kumari, Surabhi  and
      Jaiswal, Nikhil  and
      Patidar, Mayur  and
      Patwardhan, Manasi  and
      Karande, Shirish  and
      Agarwal, Puneet  and
      Vig, Lovekesh",
    editor = "Ben-David, Eyal  and
      Cohen, Shay  and
      McDonald, Ryan  and
      Plank, Barbara  and
      Reichart, Roi  and
      Rotman, Guy  and
      Ziser, Yftah",
    booktitle = "Proceedings of the Second Workshop on Domain Adaptation for NLP",
    month = apr,
    year = "2021",
    address = "Kyiv, Ukraine",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.adaptnlp-1.26",
    pages = "263--271",
    abstract = "Domain-specific Neural Machine Translation (NMT) model can provide improved performance, however, it is difficult to always access a domain-specific parallel corpus. Iterative Back-Translation can be used for fine-tuning an NMT model for a domain even if only a monolingual domain corpus is available. The quality of synthetic parallel corpora in terms of closeness to in-domain sentences can play an important role in the performance of the translation model. Recent works have shown that filtering at different stages of the back translation and weighting the sentences can provide state-of-the-art performance. In comparison, in this work, we observe that a simpler filtering approach based on a domain classifier, applied only to the pseudo-training data can consistently perform better, providing performance gains of 1.40, 1.82 and 0.76 in terms of BLEU score for Medical, Law and IT in one direction, and 1.28, 1.60 and 1.60 in the other direction in low resource scenario over competitive baselines. In the high resource scenario, our approach is at par with competitive baselines.",
}