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Computational Linguistics

Paola Merlo, Editor
December 2005, Vol. 31, No. 4, Pages 477-504
(doi: 10.1162/089120105775299168)
© 2006 Association for Computational Linguistics
Improving Machine Translation Performance by Exploiting Non-Parallel Corpora
Article PDF (1.03 MB)

We present a novel method for discovering parallel sentences in comparable, non-parallel corpora. We train a maximum entropy classifier that, given a pair of sentences, can reliably determine whether or not they are translations of each other. Using this approach, we extract parallel data from large Chinese, Arabic, and English non-parallel newspaper corpora. We evaluate the quality of the extracted data by showing that it improves the performance of a state-of-the-art statistical machine translation system. We also show that a good-quality MT system can be built from scratch by starting with a very small parallel corpus (100,000 words) and exploiting a large non-parallel corpus. Thus, our method can be applied with great benefit to language pairs for which only scarce resources are available.