@inbook{643114d278674ec29432a6b1cc405480,
title = "Multi-align: Combining linguistic and statistical techniques to improve alignments for adaptable MT",
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.",
author = "Ayan, {Necip Fazil} and Dorr, {Bonnie J.} and Nizar Habash",
year = "2004",
doi = "10.1007/978-3-540-30194-3_3",
language = "English (US)",
isbn = "3540233008",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "17--26",
editor = "Frederking, {Robert E.} and Taylor, {Kathryn B.}",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
}