@inbook {13830,
	title = {Multi-align: Combining Linguistic and Statistical Techniques to Improve Alignments for Adaptable MT},
	booktitle = {Machine Translation: From Real Users to ResearchMachine Translation: From Real Users to Research},
	series = {Lecture Notes in Computer Science},
	volume = {3265},
	year = {2004},
	month = {2004///},
	pages = {17 - 26},
	publisher = {Springer Berlin / Heidelberg},
	organization = {Springer Berlin / Heidelberg},
	abstract = {An adaptable statistical or hybrid MT system relies heavily on the quality of word-level alignments of real-world data. Statistical alignment approaches provide a reasonable initial estimate for word alignment. However, they cannot handle certain types of linguistic phenomena such as long-distance dependencies and structural differences between languages. We address this issue in Multi-Align, a new framework for incremental testing of different alignment algorithms and their combinations. Our design allows users to tune their systems to the properties of a particular genre/domain while still benefiting from general linguistic knowledge associated with a language pair. We demonstrate that a combination of statistical and linguistically-informed alignments can resolve translation divergences during the alignment process.},
	isbn = {978-3-540-23300-8},
	url = {http://dx.doi.org/10.1007/978-3-540-30194-3_3},
	author = {Ayan,Necip and Dorr, Bonnie J and Habash,Nizar},
	editor = {Frederking,Robert and Taylor,Kathryn}
}
