Latent-Variable Synchronous CFGs for Hierarchical Translation

Abstract

Data-driven refinement of non-terminal categories has been demonstrated to be a reliable technique for improving monolingual parsing with PCFGs. In this paper, we extend these techniques to learn latent refinements of single-category synchronous grammars, so as to improve translation performance. We compare two estimators for this latent-variable model: one based on EM and the other is a spectral algorithm based on the method of moments. We evaluate their performance on a Chinese–English translation task. The results indicate that we can achieve significant gains over the baseline with both approaches, but in particular the moments-based estimator is both faster and performsbetter than EM