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Cambridge, MA, United States

Ananthakrishnan S.,Speech and Language Processing Unit | Prasad R.,Speech and Language Processing Unit | Stallard D.,Speech and Language Processing Unit | Natarajan P.,Speech and Language Processing Unit
Computer Speech and Language | Year: 2013

The development of high-performance statistical machine translation (SMT) systems is contingent on the availability of substantial, in-domain parallel training corpora. The latter, however, are expensive to produce due to the labor-intensive nature of manual translation. We propose to alleviate this problem with a novel, semi-supervised, batch-mode active learning strategy that attempts to maximize in-domain coverage by selecting sentences, which represent a balance between domain match, translation difficulty, and batch diversity. Simulation experiments on an English-to-Pashto translation task show that the proposed strategy not only outperforms the random selection baseline, but also traditional active selection techniques based on dissimilarity to existing training data. © 2011 Elsevier Ltd. All rights reserved. Source

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