An Unsupervised Model for Joint Phrase Alignment and Extraction
Graham Neubig, Taro Watanabe, Eiichiro Sumita, Shinsuke Mori, Tatsuya Kawahara · 2011
We present an unsupervised model for joint phrase alignment and extraction using non-parametric Bayesian methods and inversion transduction grammars (ITGs). The key con-tribution is that phrases of many granulari-ties are included directly in the model through the use of a novel formulation that memorizes phrases generated not only by terminal, but also non-terminal symbols. This allows for a completely probabilistic model that is able to create a phrase table that achieves com-petitive accuracy on phrase-based machine translation tasks directly from unaligned sen-tence pairs. Experiments on several language pairs demonstrate that the proposed model matches the accuracy of traditional two-step word alignment/phrase extraction approach while reducing the phrase table to a fraction of the original size. 1