@inproceedings{li-etal-2014-discriminative,
    title = "A discriminative framework of integrating translation memory features into {SMT}",
    author = "Li, Liangyou  and
      Way, Andy  and
      Liu, Qun",
    editor = "Al-Onaizan, Yaser  and
      Simard, Michel",
    booktitle = "Proceedings of the 11th Conference of the Association for Machine Translation in the Americas: MT Researchers Track",
    month = oct # " 22-26",
    year = "2014",
    address = "Vancouver, Canada",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2014.amta-researchers.19/",
    pages = "249--260",
    abstract = "Combining Translation Memory (TM) with Statistical Machine Translation (SMT) together has been demonstrated to be beneficial. In this paper, we present a discriminative framework which can integrate TM into SMT by incorporating TM-related feature functions. Experiments on English{--}Chinese and English{--}French tasks show that our system using TM feature functions only from the best fuzzy match performs significantly better than the baseline phrase- based system on both tasks, and our discriminative model achieves comparable results to those of an effective generative model which uses similar features. Furthermore, with the capacity of handling a large amount of features in the discriminative framework, we propose a method to efficiently use multiple fuzzy matches which brings more feature functions and further significantly improves our system."
}