A tree-to-tree model for statistical machine translation
Name
289341212-MIT.pdf
Description
Full printable version
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17.48 MB
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Checksum (MD5)
979a213b2072afbb5bd0b13b7df19bde
Author(s)
Cowan, Brooke A. (Brooke Alissa), 1972-
Advisor(s)
Michael J. Collins.
Date Issued
2008
Publisher
Massachusetts Institute of Technology
Abstract
In this thesis, we take a statistical tree-to-tree approach to solving the problem of machine translation (MT). In a statistical tree-to-tree approach, first the source-language input is parsed into a syntactic tree structure; then the source-language tree is mapped to a target-language tree. This kind of approach has several advantages. For one, parsing the input generates valuable information about its meaning. In addition, the mapping from a source-language tree to a target-language tree offers a mechanism for preserving the meaning of the input. Finally, producing a target-language tree helps to ensure the grammaticality of the output. A main focus of this thesis is to develop a statistical tree-to-tree mapping algorithm. Our solution involves a novel representation called an aligned extended projection, or AEP. The AEP, inspired by ideas in linguistic theory related to tree-adjoining grammars, is a parse-tree like structure that models clause-level phenomena such as verbal argument structure and lexical word-order. The AEP also contains alignment information that links the source-language input to the target-language output. Instead of learning a mapping from a source-language tree to a target-language tree, the AEP-based approach learns a mapping from a source-language tree to a target-language AEP. The AEP is a complex structure, and learning a mapping from parse trees to AEPs presents a challenging machine learning problem. In this thesis, we use a linear structured prediction model to solve this learning problem. A human evaluation of the AEP-based translation approach in a German-to-English task shows significant improvements in the grammaticality of translations. This thesis also presents a statistical parser for Spanish that could be used as part of a Spanish/English translation system.
Description
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.
Includes bibliographical references (p. 227-234).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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