@inbook {13760,
	title = {DUSTer: A Method for Unraveling Cross-Language Divergences for Statistical Word-Level Alignment},
	booktitle = {Machine Translation: From Research to Real UsersMachine Translation: From Research to Real Users},
	series = {Lecture Notes in Computer Science},
	volume = {2499},
	year = {2002},
	month = {2002///},
	pages = {31 - 43},
	publisher = {Springer Berlin / Heidelberg},
	organization = {Springer Berlin / Heidelberg},
	abstract = {The frequent occurrence of divergenceS {\textemdash}structural differences between languages{\textemdash}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.},
	isbn = {978-3-540-44282-0},
	url = {http://dx.doi.org/10.1007/3-540-45820-4_4},
	author = {Dorr, Bonnie J and Pearl,Lisa and Hwa,Rebecca and Habash,Nizar},
	editor = {Richardson,Stephen}
}
