Weakly Supervised Training of Semantic Parsers

Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pages 754–765, Jeju Island, Korea, 12–14 July 2012. c⃝2012 Association for Computational Linguistics Weakly Supervised Training of Semantic Parsers Jayant Krishnamurthy Carnegie Mellon University 5000 Forbes Avenue Pittsburgh, PA 15213 jayantk@cs.cmu.edu Tom M. Mitchell Carnegie Mellon University 5000 Forbes Avenue Pittsburgh, PA 15213 tom.mitchell@cmu.edu Abstract 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 extracting logical forms from natural language queries against Freebase. On this task, the trained semantic parser achieves 80% precision and 56% recall, despite never having seen an annotated logical form. 1 Introduction Semantic parsing converts natural language state- ments into logical forms in a meaning repre- sentation language. For example, the phrase “town in California” might be represented as λx.CITY(x) ∧ LOCATEDIN(x, CALIFORNIA), where CITY, LOCATEDIN and CALIFORNIA are predicates and entities from a knowledge base. The expressiv- ity and utility of semantic parsing is derived from this meaning representation, which is essentially a program that is directly executable by a computer. In this sense, broad coverage semantic parsing is the goal of natural language understanding. Unfortunately, due to data annotation constraints, modern semantic parsers only operate in narrow do- mains. The best performing semantic parsers are trained using extensive manual annotation: typi- cally, a number of sentences must be annotated with their desired logical form. Although other forms of supervision exist (Clarke et al., 2010; Liang et al., 2011), these methods similarly require annotations for individual sentences. More automated training methods are required to produce semantic parsers with richer meaning representations. This paper presents an algorithm for training a se- mantic parser without per-sentence annotations. In- stead, our approach exploits two easily-obtainable sources of supervision: a large knowledge base and (automatically) dependency-parsed sentences. The semantic parser is trained to identify relation in- stances from the knowledge base while simulta- neously producing parses that syntactically agree with the dependency parses. Combining these two sources of supervision allows us to train an accurate semantic parser for any knowledge base without an- notated training data. We demonstrate our approach by training a Com- binatory Categorial Grammar (CCG) (Steedman, 1996) that parses sentences into logical forms con- taining any of 77 relations from Freebase. Our training data consists of relation instances from Freebase and automatically dependency-parsed sen- tences from a web corpus. The …

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