Paper: Latent-Variable Synchronous CFGs for Hierarchical Translation

ACL ID D14-1210
Title Latent-Variable Synchronous CFGs for Hierarchical Translation
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2014

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.