@inproceedings{miculicich-etal-2018-document,
    title = "Document-Level Neural Machine Translation with Hierarchical Attention Networks",
    author = "Miculicich, Lesly  and
      Ram, Dhananjay  and
      Pappas, Nikolaos  and
      Henderson, James",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1325",
    doi = "10.18653/v1/D18-1325",
    pages = "2947--2954",
    abstract = "Neural Machine Translation (NMT) can be improved by including document-level contextual information. For this purpose, we propose a hierarchical attention model to capture the context in a structured and dynamic manner. The model is integrated in the original NMT architecture as another level of abstraction, conditioning on the NMT model{'}s own previous hidden states. Experiments show that hierarchical attention significantly improves the BLEU score over a strong NMT baseline with the state-of-the-art in context-aware methods, and that both the encoder and decoder benefit from context in complementary ways.",
}