Combining phrase-based and tree-to-tree translation

We present a novel approach to multi-engine machine translation, using a feature-based classification algorithm. Instead of just using language models, translation models, or internal confidence scores, we sought out other features that could be used to determine which of two translations to select. We combined the outputs from a phrase-based system, Moses [Koehn et al., 2007] and a tree-to-tree system [Cowan et al., 2006]. Our main result is a 0.3 to 0.4 improvement in BLEU score over the best single system used, while also improving fluency and adequacy judgments. In addition, we used the same setup to directly predict which sentences would be judged by humans to be more fluent and more adequate. In those domains, we predicted the better sentence 6% to 7% more often than a baseline of always choosing the single best system.

Department dc:contributor.department

Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2008

Author dc:creator

• Lieberman, Michael (Michael R.)

Advisor dc:contributor.advisor

• Michael Collins.

• Electrical Engineering and Computer Science.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/45635

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/45635