Selective Sampling Using the Query by Committee Algorithm
Machine Learning
BLEU: a method for automatic evaluation of machine translation
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Statistical phrase-based translation
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Minimum error rate training in statistical machine translation
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Confidence estimation for machine translation
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Active learning for statistical phrase-based machine translation
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Discriminative sample selection for statistical machine translation
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
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The availability of substantial, in-domain parallel corpora is critical for the development of high-performance statistical machine translation (SMT) systems. Such corpora, 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 indomain 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 learning techniques based on dissimilarity to existing training data. Our approach achieves a relative improvement of 45.9% in BLEU over the seed baseline, while the closest competitor gained only 24.8% with the same number of selected sentences.