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Extracting parallel phrases from comparable data for machine translation†

Published online by Cambridge University Press:  15 June 2016

SANJIKA HEWAVITHARANA  and
STEPHAN VOGEL
SANJIKA HEWAVITHARANA
Affiliation:
Raytheon BBN Technologies, Cambridge, MA 02138, USA email: [email protected]
STEPHAN VOGEL
Affiliation:
Qatar Computing Research Institute, Doha, Qatar email: [email protected]
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Abstract

Mining parallel data from comparable corpora is a promising approach for overcoming the data sparseness in statistical machine translation and other natural language processing applications. In this paper, we address the task of detecting parallel phrase pairs embedded in comparable sentence pairs. We present a novel phrase alignment approach that is designed to only align parallel sections bypassing non-parallel sections of the sentence. We compare the proposed approach with two other alignment methods: (1) the standard phrase extraction algorithm, which relies on the Viterbi path of the word alignment, (2) a binary classifier to detect parallel phrase pairs when presented with a large collection of phrase pair candidates. We evaluate the accuracy of these approaches using a manually aligned data set, and show that the proposed approach outperforms the other two approaches. Finally, we demonstrate the effectiveness of the extracted phrase pairs by using them in Arabic–English and Urdu–English translation systems, which resulted in improvements upto 1.2 Bleu over the baseline. The main contributions of this paper are two-fold: (1) novel phrase alignment algorithms to extract parallel phrase pairs from comparable sentences, (2) evaluating the utility of the extracted phrases by using them directly in the MT decoder.

Type
Articles
Information
Natural Language Engineering , Volume 22 , Issue 4: Machine Translation Using Comparable Corpora , July 2016 , pp. 549 - 573
Copyright
Copyright © Cambridge University Press 2016 

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Footnotes

†

Part of this work was conducted when the authors were affiliated to the Language Technologies Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA.

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