@inproceedings{sugiyama-yoshinaga-2021-context,
    title = "Context-aware Decoder for Neural Machine Translation using a Target-side Document-Level Language Model",
    author = "Sugiyama, Amane  and
      Yoshinaga, Naoki",
    editor = "Toutanova, Kristina  and
      Rumshisky, Anna  and
      Zettlemoyer, Luke  and
      Hakkani-Tur, Dilek  and
      Beltagy, Iz  and
      Bethard, Steven  and
      Cotterell, Ryan  and
      Chakraborty, Tanmoy  and
      Zhou, Yichao",
    booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2021.naacl-main.461/",
    doi = "10.18653/v1/2021.naacl-main.461",
    pages = "5781--5791",
    abstract = "Although many end-to-end context-aware neural machine translation models have been proposed to incorporate inter-sentential contexts in translation, these models can be trained only in domains where parallel documents with sentential alignments exist. We therefore present a simple method to perform context-aware decoding with any pre-trained sentence-level translation model by using a document-level language model. Our context-aware decoder is built upon sentence-level parallel data and target-side document-level monolingual data. From a theoretical viewpoint, our core contribution is the novel representation of contextual information using point-wise mutual information between context and the current sentence. We demonstrate the effectiveness of our method on English to Russian translation, by evaluating with BLEU and contrastive tests for context-aware translation."
}