Paper: Learning Compact Lexicons for CCG Semantic Parsing

ACL ID D14-1134
Title Learning Compact Lexicons for CCG Semantic Parsing
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2014
Authors

We present methods to control the lexicon size when learning a Combinatory Cate- gorial Grammar semantic parser. Existing methods incrementally expand the lexicon by greedily adding entries, considering a single training datapoint at a time. We pro- pose using corpus-level statistics for lexi- con learning decisions. We introduce vot- ing to globally consider adding entries to the lexicon, and pruning to remove entries no longer required to explain the training data. Our methods result in state-of-the-art performance on the task of executing se- quences of natural language instructions, achieving up to 25% error reduction, with lexicons that are up to 70% smaller and are qualitatively less noisy.