Paper: Weakly Supervised Training of Semantic Parsers

ACL ID D12-1069
Title Weakly Supervised Training of Semantic Parsers
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2012

We present a method for training a semantic parser using only a knowledge base and an un- labeled text corpus, without any individually annotated sentences. Our key observation is that multiple forms of weak supervision can be combined to train an accurate semantic parser: semantic supervision from a knowledge base, and syntactic supervision from dependency- parsed sentences. We apply our approach to train a semantic parser that uses 77 rela- tions from Freebase in its knowledge repre- sentation. This semantic parser extracts in- stances of binary relations with state-of-the- art accuracy, while simultaneously recovering much richer semantic structures, such as con- junctions of multiple relations with partially shared arguments. We demonstrate recovery of this richer structure by extracti...