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A Semi-Supervised Batch-Mode Active Learning Strategy for Improved Statistical Machine Translation
2010
Conference on Computational Natural Language Learning
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, semisupervised, batch-mode active learning strategy that attempts to maximize indomain coverage by selecting sentences, which represent a balance between domain match, translation
dblp:conf/conll/AnanthakrishnanPSN10
fatcat:gzyispsn75hwzeibo264kdd6b4