In Proceedings of the Twelfth Conference on Computational Natural Language Learning (CoNLL-2008) , pp. 33-40, Manchester, UK, August 2008.
Paper ID: 222
Category: Learning for Semantic Parsing, Natural Language Learning
A semantic parser learning system learns to map natural language sentences into their domain-specific formal meaning representations, but if the constructs of the meaning representation language do not correspond well with the natural language then the system may not learn a good semantic parser. This paper presents approaches for automatically transforming a meaning representation grammar (MRG) to conform it better with the natural language semantics. It introduces grammar transformation operators and meaning representation macros which are applied in an error-driven manner to transform an MRG while training a semantic parser learning system. Experimental results show that the automatically transformed MRGs lead to better learned semantic parsers which perform comparable to the semantic parsers learned using manually engineered MRGs.
