@inproceedings{ayan-etal-2004-multi,
title = "Multi-Align: combining linguistic and statistical techniques to improve alignments for adaptable {MT}",
author = "Ayan, Necip Fazil and
Dorr, Bonnie and
Habash, Nizar",
editor = "Frederking, Robert E. and
Taylor, Kathryn B.",
booktitle = "Proceedings of the 6th Conference of the Association for Machine Translation in the Americas: Technical Papers",
month = sep # " 28 - " # oct # " 2",
year = "2004",
address = "Washington, USA",
publisher = "Springer",
url = "https://link.springer.com/chapter/10.1007/978-3-540-30194-3_3",
pages = "17--26",
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.",
}
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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
%Y Frederking, Robert E.
%Y Taylor, Kathryn B.
%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)
ACL