CLAIRE makes machine translation BLEU no more

We introduce CLAIRE, a mathematically principled model for inferring ranks and scores for arbitrary items based on forced-choice binary comparisons, and show how to apply this technique to statistical models to take advantage of problem-specific assistance from non-experts. We apply this technique to two language processing problems: parsing and machine translation. This leads to an analysis which casts doubts on modern evaluation methods for machine translation systems, and an application of CLAIRE as a new technique for evaluating machine translation systems which is inexpensive, has theoretical guarantees, and correlates strongly in practice with more expensive human judgments of system quality. Our analysis reverses several major tenants of the mainstream machine translation research agenda, suggesting in particular that the use of linguistic models should be reexamined.

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

2012

Author dc:creator

• Mohammad, Ali (Ali H.)

Advisor dc:contributor.advisor

• Boris Katz.

• 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/1721.1/75653

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/75653