NDLI: Efficient data selection for machine translation

Content Provider	IEEE Xplore Digital Library
Author	Mandal, A. Vergyri, D. Wang, W. Zheng, J. Stolcke, A. Tur, G. Hakkani-Tur, D. Ayan, N.F.
Copyright Year	2008
Description	Author affiliation: Int. Comput. Sci. Inst., Berkeley, CA (Hakkani-Tur, D.) \|\| Speech Technol. & Res. Lab., SRI Int., Menlo Park, CA (Mandal, A.; Vergyri, D.; Wang, W.; Zheng, J.; Stolcke, A.; Tur, G.; Ayan, N.F.)
Abstract	Performance of statistical machine translation (SMT) systems relies on the availability of a large parallel corpus which is used to estimate translation probabilities. However, the generation of such corpus is a long and expensive process. In this paper, we introduce two methods for efficient selection of training data to be translated by humans. Our methods are motivated by active learning and aim to choose new data that adds maximal information to the currently available data pool. The first method uses a measure of disagreement between multiple SMT systems, whereas the second uses a perplexity criterion. We performed experiments on Chinese-English data in multiple domains and test sets. Our results show that we can select only one-fifth of the additional training data and achieve similar or better translation performance, compared to that of using all available data.
Starting Page	261
Ending Page	264
File Size	237645
Page Count	4
File Format	PDF
ISBN	9781424434718
DOI	10.1109/SLT.2008.4777890
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-12-15
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Availability System testing Surface-mount technology data selection Natural languages Training data Humans Web pages Probability machine translation Information retrieval Speech
Content Type	Text
Resource Type	Article

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