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A unified alignment algorithm for bilingual data

Published online by Cambridge University Press:  13 September 2011

CHRISTOPH TILLMANN  and
SANJIKA HEWAVITHARANA
CHRISTOPH TILLMANN
Affiliation:
IBM T.J. Watson Research Center, Yorktown Heights, New York, NY 10598, USA email: ctill@us.ibm.com
SANJIKA HEWAVITHARANA
Affiliation:
Carnegie Mellon University, Pittsburgh, PA 15213, USA email: sanjika@cs.cmu.edu
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Abstract

The paper presents a novel unified algorithm for aligning sentences with their translations in bilingual data. With the help of ideas from a stack-based dynamic programming decoder for speech recognition (Ney 1984), the search is parametrized in a novel way such that the unified algorithm can be used on various types of data that have been previously handled by separate implementations: the extracted text chunk pairs can be either sub-sentential pairs, one-to-one, or many-to-many sentence-level pairs. The one-stage search algorithm is carried out in a single run over the data. Its memory requirements are independent of the length of the source document, and it is applicable to sentence-level parallel as well as comparable data. With the help of a unified beam-search candidate pruning, the algorithm is very efficient: it avoids any document-level pre-filtering and uses less restrictive sentence-level filtering. Results are presented on a Russian–English, a Spanish–English, and an Arabic–English extraction task. Based on simple word-based scoring features, text chunk pairs are extracted out of several trillion candidates, where the search is carried out on 300 processors in parallel.

Type
Articles
Information
Natural Language Engineering , Volume 19 , Issue 1 , January 2013 , pp. 33 - 60
Copyright
Copyright © Cambridge University Press 2011

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