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2004
Springer

Multi-align: Combining Linguistic and Statistical Techniques to Improve Alignments for Adaptable MT

10 years 13 days ago
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.
Necip Fazil Ayan, Bonnie J. Dorr, Nizar Habash
Added 30 Jun 2010
Updated 30 Jun 2010
Type Conference
Year 2004
Where AMTA
Authors Necip Fazil Ayan, Bonnie J. Dorr, Nizar Habash
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