2026-09-23T00:57:04Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/465352022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Michael Collins.
Wheeler, Dan Lowe
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2009-08-26T16:44:27Z
2009-08-26T16:44:27Z
2008
2008
http://hdl.handle.net/1721.1/46535
416602511
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.
Includes bibliographical references (p. 80-82).
Language pairs such as Chinese and English with largely differing word order have proved to be one of the greatest challenges in statistical machine translation. One reason is that such techniques usually work with sentences as flat strings of words, rather than explicitly attempting to parse any sort of hierarchical structural representation. Because even simple syntactic differences between languages can quickly lead to a universe of idiosyncratic surface level word reordering rules, many believe the near future of machine translation will lie heavily in syntactic modeling. The time to start may be now: advances in statistical parsing over the last decade have already started opening the door. Following the work of Cowan et al., I present a statistical tree-to-tree translation system for Chinese to English that formulates the translation step as a prediction of English clause structure from Chinese clause structure. Chinese sentences are segmented and parsed, split into clauses, and independently translated into English clauses using a discriminative feature based model. Clausal arguments, such as subject and object, are translated separately using an off-the-shelf phrase-based translator. By explicitly modeling syntax at a clausal level, but using a phrase-based (flat-sentence) method on local, reduced expressions, such as clausal arguments, I aim to address the current weakness in long-distance word reordering while still leveraging the excellent local translations that today's state of the art has to offer.
by Dan Lowe Wheeler.
M.Eng.
293 p.
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by
copyright. They may be viewed from this source for any purpose, but
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http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Machine translation through clausal syntax : a statistical approach for Chinese to English
Incorporating syntax into machine translation : a statistical approach for Chinese to English
Thesis
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<Abstract>Language pairs such as Chinese and English with largely differing word order have proved to be one of the greatest challenges in statistical machine translation. One reason is that such techniques usually work with sentences as flat strings of words, rather than explicitly attempting to parse any sort of hierarchical structural representation. Because even simple syntactic differences between languages can quickly lead to a universe of idiosyncratic surface level word reordering rules, many believe the near future of machine translation will lie heavily in syntactic modeling. The time to start may be now: advances in statistical parsing over the last decade have already started opening the door. Following the work of Cowan et al., I present a statistical tree-to-tree translation system for Chinese to English that formulates the translation step as a prediction of English clause structure from Chinese clause structure. Chinese sentences are segmented and parsed, split into clauses, and independently translated into English clauses using a discriminative feature based model. Clausal arguments, such as subject and object, are translated separately using an off-the-shelf phrase-based translator. By explicitly modeling syntax at a clausal level, but using a phrase-based (flat-sentence) method on local, reduced expressions, such as clausal arguments, I aim to address the current weakness in long-distance word reordering while still leveraging the excellent local translations that today's state of the art has to offer.</Abstract>
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