An empirical study on web mining of parallel data

This paper¹ presents an empirical approach to mining parallel corpora. Conventional approaches use a readily available collection of comparable, nonparallel corpora to extract parallel sentences. This paper attempts the much more challenging task of directly searching for high-quality sentence pairs from the Web. We tackle the problem by formulating good search query using Learning to Rank? and by filtering noisy document pairs using IBM Model 1 alignment. End-to-end evaluation shows that the proposed approach significantly improves the performance of statistical machine translation.