Natural language understanding and dialog management are two integral components of interactive dialog systems. Previous research has used machine learning techniques to individually optimize these components, with different forms of direct and indirect supervision. We present an approach to integrate the learning of both a dialog strategy using reinforcement learning, and a semantic parser for robust natural language understanding, using only natural dialog interaction for supervision. Experimental results on a simulated task of robot instruction demonstrate that joint learning of both components improves dialog performance over learning either of these components alone.

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In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2017), pp. 547--557, Valencia, Spain, April 2017.

@inproceedings{padmakumar:eacl17, title={Integrated Learning of Dialog Strategies and Semantic Parsing}, author={Aishwarya Padmakumar and Jesse Thomason and Raymond J. Mooney}, booktitle={Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2017)}, month={April}, address={Valencia, Spain}, pages={547--557}, url="http://www.cs.utexas.edu/users/ai-labpub-view.php?PubID=127615", year={2017} }