@inproceedings{wu-wang-2004-improving-domain,
    title = "Improving domain-specific word alignment with a general bilingual corpus",
    author = "Wu, Hua  and
      Wang, Haifeng",
    editor = "Frederking, Robert E.  and
      Taylor, Kathryn B.",
    booktitle = "Proceedings of the 6th Conference of the Association for Machine Translation in the Americas: Technical Papers",
    month = sep # " 28 - " # oct # " 2",
    year = "2004",
    address = "Washington, USA",
    publisher = "Springer",
    url = "https://preview.aclanthology.org/fix-sig-urls/2004.amta-papers.29/",
    pages = "262--271",
    abstract = "In conventional word alignment methods, some employ statistical models or statistical measures, which need large-scale bilingual sentence-aligned training corpora. Others employ dictionaries to guide alignment selection. However, these methods achieve unsatisfactory alignment results when performing word alignment on a small-scale domain-specific bilingual corpus without terminological lexicons. This paper proposes an approach to improve word alignment in a specific domain, in which only a small-scale domain-specific corpus is available, by adapting the word alignment information in the general domain to the specific domain. This approach first trains two statistical word alignment models with the large-scale corpus in the general domain and the small-scale corpus in the specific domain respectively, and then improves the domain-specific word alignment with these two models. Experimental results show a significant improvement in terms of both alignment precision and recall, achieving a relative error rate reduction of 21.96{\%} as compared with state-of-the-art technologies."
}