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.

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In Proceedings of the Twelfth Conference on Computational Natural Language Learning (CoNLL-2008), pp. 33--40, Manchester, UK, August 2008.

@inproceedings{kate:conll08, title={Transforming Meaning Representation Grammars to Improve Semantic Parsing}, author={Rohit J. Kate}, booktitle={Proceedings of the Twelfth Conference on Computational Natural Language Learning (CoNLL-2008)}, month={August}, address={Manchester, UK}, pages={33--40}, url="http://www.cs.utexas.edu/users/ai-lab?kate:conll08", year={2008} }