@inproceedings{zhang-etal-2014-probabilistic,
    title = "A probabilistic feature-based fill-up for {SMT}",
    author = "Zhang, Jian  and
      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/ingest_wac_2008/2014.amta-researchers.8/",
    pages = "96--109",
    abstract = "In this paper, we describe an effective translation model combination approach based on the estimation of a probabilistic Support Vector Machine (SVM). We collect domain knowledge from both in-domain and general-domain corpora inspired by a commonly used data selection algorithm, which we then use as features for the SVM training. Drawing on previous work on binary-featured phrase table fill-up (Nakov, 2008; Bisazza et al., 2011), we substitute the binary feature in the original work with our probabilistic domain-likeness feature. Later, we design two experiments to evaluate the proposed probabilistic feature-based approach on the French-to-English language pair using data provided at WMT07, WMT13 and IWLST11 translation tasks. Our experiments demonstrate that translation performance can gain significant improvements of up to +0.36 and +0.82 BLEU scores by using our probabilistic feature-based translation model fill-up approach compared with the binary featured fill-up approach in both experiments."
}