Models for Synchronous Grammar Induction

The last decade of research in Statistical Machine Translation (SMT) has seen rapid progress. The most successful methods have been based on synchronous context free grammars (SCFGs), which encode translational equivalences and license reordering between tokens in the source and target languages. Yet, while closely related language pairs can be translated with a high degree of precision now, the result for distant pairs is far from acceptable. In theory, however, the “right” SCFG is capable of handling most, if not all, structurally divergent language pairs. The 2010 Language Engineering Workshop Models of Synchronous Grammar Induction for SMT had the goal to focus on the crucial practical aspects of acquiring such SCFGs from bilingual text. We started with existing algorithms for inducing unlabeled SCFGs (e.g. the popular Hiero model) and then used state-of-the-art unsupervised learning methods to refine the syntactic constituents used in the translation rules of the grammar.