@inproceedings{dorr-etal-2002-duster,
    title = "{DUST}er: a method for unraveling cross-language divergences for statistical word-level alignment",
    author = "Dorr, Bonnie  and
      Pearl, Lisa  and
      Hwa, Rebecca  and
      Habash, Nizar",
    editor = "Richardson, Stephen D.",
    booktitle = "Proceedings of the 5th Conference of the Association for Machine Translation in the Americas: Technical Papers",
    month = oct # " 8-12",
    year = "2002",
    address = "Tiburon, USA",
    publisher = "Springer",
    url = "https://preview.aclanthology.org/fix-sig-urls/2002.amta-papers.4/",
    pages = "31--43",
    abstract = "The frequent occurrence of divergenceS{---}structural differences between languages{---}presents a great challenge for statistical word-level alignment. In this paper, we introduce DUSTer, a method for systematically identifying common divergence types and transforming an English sentence structure to bear a closer resemblance to that of another language. Our ultimate goal is to enable more accurate alignment and projection of dependency trees in another language without requiring any training on dependency-tree data in that language. We present an empirical analysis comparing the complexities of performing word-level alignments with and without divergence handling. Our results suggest that our approach facilitates word-level alignment, particularly for sentence pairs containing divergences."
}