Diversify and Combine: Improving Word Alignment for Machine Translation on Low-Resource Languages

We present a novel method to improve word alignment quality and eventually the translation performance by producing and combining complementary word alignments for low-resource languages. Instead of focusing on the improvement of a single set of word alignments, we generate multiple sets of diversified alignments based on different motivations, such as linguistic knowledge, morphology and heuristics. We demonstrate this approach on an English-to-Pashto translation task by combining the alignments obtained from syntactic reordering, stemming, and partial words. The combined alignment outperforms the baseline alignment, with significantly higher F-scores and better translation performance.

• Type: Journal
• Year: 2010
• Where: ACL
• Authors: Bing Xiang, Yonggang Deng, Bowen Zhou
• Keywords: ACL 2010, Complementary Word Alignments, Computational Linguistics, Translation Performance, Word Alignments