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    <name type="personal">
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    <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.</abstract>
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%0 Conference Proceedings
%T Multi-Align: combining linguistic and statistical techniques to improve alignments for adaptable MT
%A Ayan, Necip Fazil
%A Dorr, Bonnie
%A Habash, Nizar
%S Proceedings of the 6th Conference of the Association for Machine Translation in the Americas: Technical Papers
%D 2004
%8 sep" 28  "oct" 2"
%I Springer
%C Washington, USA
%F ayan-etal-2004-multi
%X 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.
%U https://link.springer.com/chapter/10.1007/978-3-540-30194-3_3
%P 17-26

Markdown (Informal)

[Multi-Align: combining linguistic and statistical techniques to improve alignments for adaptable MT](https://link.springer.com/chapter/10.1007/978-3-540-30194-3_3) (Ayan et al., AMTA 2004)

• Multi-Align: combining linguistic and statistical techniques to improve alignments for adaptable MT (Ayan et al., AMTA 2004)

ACL

• Necip Fazil Ayan, Bonnie Dorr, and Nizar Habash. 2004. Multi-Align: combining linguistic and statistical techniques to improve alignments for adaptable MT. In Proceedings of the 6th Conference of the Association for Machine Translation in the Americas: Technical Papers, pages 17–26, Washington, USA. Springer.