Combining Top-down and Bottom-up Search for Unsupervised Induction of Transduction Grammars
Markus Saers, Karteek Addanki, Dekai Wu · North American Chapter of the Association for Computational Linguistics · 2013
We show that combining both bottom-up rule chunking and top-down rule segmentation search strategies in purely unsupervised learning of phrasal inversion transduction grammars yields significantly better translation accuracy than either strategy alone. Previous approaches have relied on incrementally building larger rules by chunking smaller rules bottomup; we introduce a complementary top-down model that incrementally builds shorter rules by segmenting larger rules. Specifically, we combine iteratively chunked rules from Saers et al. (2012) with our new iteratively segmented rules. These integrate seamlessly because both stay strictly within a pure transduction grammar framework inducing under matching models during both training and testing—instead of decoding under a completely different model architecture than what is assumed during the training phases, which violates an elementary principle of machine learning and statistics. To be able to drive induction top-down, we introduce a minimum description length objective that trades off maximum likelihood against model size. We show empirically that combining the more liberal rule chunking model with a more conservative rule segmentation model results in significantly better translations than either strategy in isolation.