Automatic Parallel Fragment Extraction from Noisy Data
Jason Riesa, Daniel Marcu · 2012
We present a novel method to detect parallel fragments within noisy parallel corpora. Isolat-ing these parallel fragments from the noisy data in which they are contained frees us from noisy alignments and stray links that can severely constrain translation-rule extraction. We do this with existing machinery, making use of an existing word alignment model for this task. We evaluate the quality and utility of the ex-tracted data on large-scale Chinese-English and Arabic-English translation tasks and show sig-nificant improvements over a state-of-the-art baseline. 1