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Improving syntactic rule extraction through deleting spurious links with translation span alignment

Published online by Cambridge University Press:  06 September 2013

JINGBO ZHU ,
QIANG LI  and
TONG XIAO
JINGBO ZHU
Affiliation:
Natural Language Processing Laboratory, Northeastern University, Shenyang 110819, China e-mail: [email protected], [email protected], [email protected]
QIANG LI
Affiliation:
Natural Language Processing Laboratory, Northeastern University, Shenyang 110819, China e-mail: [email protected], [email protected], [email protected]
TONG XIAO
Affiliation:
Natural Language Processing Laboratory, Northeastern University, Shenyang 110819, China e-mail: [email protected], [email protected], [email protected]
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Abstract

Most statistical machine translation systems typically rely on word alignments to extract translation rules. This approach would suffer from a practical problem that even one spurious word alignment link can prevent some desirable translation rules from being extracted. To address this issue, this paper presents two approaches, referred to as sub-tree alignment and phrase-based forced decoding methods, to automatically learn translation span alignments from parallel data. Then, we improve the translation rule extraction by deleting spurious links and inserting new links based on bilingual translation span correspondences. Some comparison experiments are designed to demonstrate the effectiveness of the proposed approaches.

Type
Articles
Information
Natural Language Engineering , Volume 21 , Issue 2 , March 2015 , pp. 227 - 249
Copyright
Copyright © Cambridge University Press 2013 

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