@inproceedings{bisazza-etal-2011-fill,
    title = "Fill-up versus interpolation methods for phrase-based {SMT} adaptation",
    author = "Bisazza, Arianna  and
      Ruiz, Nick  and
      Federico, Marcello",
    editor = {Federico, Marcello  and
      Hwang, Mei-Yuh  and
      R{\"o}dder, Margit  and
      St{\"u}ker, Sebastian},
    booktitle = "Proceedings of the 8th International Workshop on Spoken Language Translation: Evaluation Campaign",
    month = dec # " 8-9",
    year = "2011",
    address = "San Francisco, California",
    url = "https://preview.aclanthology.org/fix-sig-urls/2011.iwslt-evaluation.18/",
    pages = "136--143",
    abstract = "This paper compares techniques to combine diverse parallel corpora for domain-specific phrase-based SMT system training. We address a common scenario where little in-domain data is available for the task, but where large background models exist for the same language pair. In particular, we focus on phrase table fill-up: a method that effectively exploits background knowledge to improve model coverage, while preserving the more reliable information coming from the in-domain corpus. We present experiments on an emerging transcribed speech translation task {--} the TED talks. While performing similarly in terms of BLEU and NIST scores to the popular log-linear and linear interpolation techniques, filled-up translation models are more compact and easy to tune by minimum error training."
}