Agreement-based Learning of Parallel Lexicons and Phrases from Non-Parallel Corpora
Table 4: Results on domain adaptation for machine translation.
['Iteration', 'Corpus Size E→ C', 'Corpus Size C→ E', 'Corpus Size Outer', 'Corpus Size Inner', 'BLEU E→ C', 'BLEU C→ E', 'BLEU Outer', 'BLEU Inner']
At iteration 0, only the out-domain corpus is used and the BLEU score is 5.61. All methods iteratively extract parallel phrases from non-parallel corpora and enlarge the extracted parallel corpus. We find that agreement-based learning achieves much higher BLEU scores while obtains a smaller parallel corpus as compared with independent learning. One possible reason is that the agreement-based learning rules out most unlikely phrase pairs by encouraging consensus between two models.