@inproceedings{stojanovski-fraser-2021-addressing,
    title = "Addressing Zero-Resource Domains Using Document-Level Context in Neural Machine Translation",
    author = "Stojanovski, Dario  and
      Fraser, Alexander",
    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://preview.aclanthology.org/landing_page/2021.adaptnlp-1.9/",
    pages = "80--93",
    abstract = "Achieving satisfying performance in machine translation on domains for which there is no training data is challenging. Traditional supervised domain adaptation is not suitable for addressing such zero-resource domains because it relies on in-domain parallel data. We show that when in-domain parallel data is not available, access to document-level context enables better capturing of domain generalities compared to only having access to a single sentence. Having access to more information provides a more reliable domain estimation. We present two document-level Transformer models which are capable of using large context sizes and we compare these models against strong Transformer baselines. We obtain improvements for the two zero-resource domains we study. We additionally provide an analysis where we vary the amount of context and look at the case where in-domain data is available."
}