Data-driven refinement of non-terminal categories has been demonstrated to be a reliable technique for improving mono-lingual parsing with PCFGs. In this pa-per, we extend these techniques to learn latent refinements of single-category syn-chronous 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 spec-tral algorithm based on the method of mo-ments. We evaluate their performance on a Chinese–English translation task. The re-sults indicate that we can achieve signifi-cant gains over the baseline with both ap-proaches, but in particular the moments-based estimator is both faster and performs better than EM.
