@inproceedings{mascarell-2017-lexical,
    title = "Lexical Chains meet Word Embeddings in Document-level Statistical Machine Translation",
    author = "Mascarell, Laura",
    editor = {Webber, Bonnie  and
      Popescu-Belis, Andrei  and
      Tiedemann, J{\"o}rg},
    booktitle = "Proceedings of the Third Workshop on Discourse in Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Ingest-2025-COMPUTEL/W17-4813/",
    doi = "10.18653/v1/W17-4813",
    pages = "99--109",
    abstract = "Currently under review for EMNLP 2017 The phrase-based Statistical Machine Translation (SMT) approach deals with sentences in isolation, making it difficult to consider discourse context in translation. This poses a challenge for ambiguous words that need discourse knowledge to be correctly translated. We propose a method that benefits from the semantic similarity in lexical chains to improve SMT output by integrating it in a document-level decoder. We focus on word embeddings to deal with the lexical chains, contrary to the traditional approach that uses lexical resources. Experimental results on German-to-English show that our method produces correct translations in up to 88{\%} of the changes, improving the translation in 36{\%}-48{\%} of them over the baseline."
}