@inproceedings{0da5356e0c8a47d3ac1e035476fe78fb,
title = "DUSTer: A method for unraveling cross-language divergences for statistical word-level alignment",
abstract = "The frequent occurrence of divergences—structural differences between languages—presents a great challenge for statistical wordlevel 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.",
author = "Dorr, {Bonnie J.} and Lisa Pearl and Rebecca Hwa and Nizar Habash",
note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 2002.; 5th Conference of the Association for Machine Translation in the Americas, AMTA 2002 ; Conference date: 08-10-2002 Through 12-10-2002",
year = "2002",
doi = "10.1007/3-540-45820-4_4",
language = "English (US)",
isbn = "3540442820",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "31--43",
editor = "Richardson, {Stephen D.}",
booktitle = "Machine Translation",
}