Learning for Semantic Parsing with Statistical Machine Translation

Proceedings of the Human Language Technology Conference of the North American Chapter of the ACL, pages 439–446, New York, June 2006. c⃝2006 Association for Computational Linguistics Learning for Semantic Parsing with Statistical Machine Translation Yuk Wah Wong and Raymond J. Mooney Department of Computer Sciences The University of Texas at Austin 1 University Station C0500 Austin, TX 78712-0233, USA {ywwong,mooney}@cs.utexas.edu Abstract We present a novel statistical approach to semantic parsing, WASP, for construct- ing a complete, formal meaning represen- tation of a sentence. A semantic parser is learned given a set of sentences anno- tated with their correct meaning represen- tations. The main innovation of WASP is its use of state-of-the-art statistical ma- chine translation techniques. A word alignment model is used for lexical acqui- sition, and the parsing model itself can be seen as a syntax-based translation model. We show that WASP performs favorably in terms of both accuracy and coverage compared to existing learning methods re- quiring similar amount of supervision, and shows better robustness to variations in task complexity and word order. 1 Introduction Recent work on natural language understanding has mainly focused on shallow semantic analysis, such as semantic role labeling and word-sense disam- biguation. This paper considers a more ambi- tious task of semantic parsing, which is the con- struction of a complete, formal, symbolic, mean- ing representation (MR) of a sentence. Seman- tic parsing has found its way in practical applica- tions such as natural-language (NL) interfaces to databases (Androutsopoulos et al., 1995) and ad- vice taking (Kuhlmann et al., 2004). Figure 1 shows a sample MR written in a meaning-representation language (MRL) called CLANG, which is used for ((bowner our {4}) (do our {6} (pos (left (half our))))) If our player 4 has the ball, then our player 6 should stay in the left side of our half. Figure 1: A meaning representation in CLANG encoding coach advice given to simulated soccer- playing agents (Kuhlmann et al., 2004). Prior research in semantic parsing has mainly fo- cused on relatively simple domains such as ATIS (Air Travel Information Service) (Miller et al., 1996; Papineni et al., 1997; Macherey et al., 2001), in which a typcial MR is only a single semantic frame. Learning methods have been devised that can gen- erate MRs with a complex, nested structure (cf. Figure 1). However, these methods are mostly based on deterministic parsing (Zelle and Mooney, 1996; Kate et al., 2005), which lack the robustness that characterizes recent advances in statistical NLP. Other learning methods involve the use of fully- annotated augmented parse trees (Ge and Mooney, 2005) or prior knowledge of the NL syntax (Zettle- moyer and Collins, 2005) in training, and hence re- quire extensive human efforts when porting to a new domain or language. In this paper, we present a novel statistical ap- proach to semantic parsing which can handle MRs with a nested structure, based on previous work on semantic parsing using transformation rules (Kate et al., 2005). The algorithm learns a semantic parser …

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