Word Alignment with Synonym Regularization

Hiroyuki Shindo, Akinori Fujino, Masaaki Nagata

Abstract: We present a novel framework for word alignment that incorporates synonym knowledge collected from monolingual linguistic resources in a bilingual probabilistic model. Synonym information is helpful for word alignment because we can expect a synonym to correspond to the same word in a different language. We design a generative model for word alignment that uses synonym information as a regularization term. The experimental results show that our proposed method significantly improves word alignment quality.