@inproceedings{farzindar-khreich-2012-evaluation,
    title = "Evaluation of Domain Adaptation Techniques for {TRANSLI} in a Real-World Environment",
    author = "Farzindar, Atefeh  and
      Khreich, Wael",
    booktitle = "Proceedings of the 10th Conference of the Association for Machine Translation in the Americas: Commercial MT User Program",
    month = oct # " 28-" # nov # " 1",
    year = "2012",
    address = "San Diego, California, USA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://preview.aclanthology.org/fix-sig-urls/2012.amta-commercial.6/",
    abstract = "Statistical Machine Translation (SMT) systems specialized for one domain often perform poorly when applied to other domains. Domain adaptation techniques allow SMT models trained from a source domain with abundant data to accommodate different target domains with limited data. This paper evaluates the performance of two adaptive techniques based on log-linear and mixture models on data from the legal domain in real-world settings. Performance evaluation includes post-editing time and effort required by a professional post-editor to improve the quality of machine-generated translations to meet industry standards, as well as traditional automated scoring techniques (BLEU scores). Results indicates that the domain adaptation techniques can yield a significant increase in BLEU score (up to three points) and a significant reduction in post-editing time of about one second per word in an operational environment."
}