Dialog systems research has primarily been focused around two main types of applications – task-oriented dialog systems that learn to use clarification to aid in understanding a goal, and open-ended dialog systems that are expected to carry out unconstrained “chit chat” conversations. However, dialog interactions can also be used to obtain various types of knowledge that can be used to improve an underlying language understanding system, or other machine learning systems that the dialog acts over. In this position paper, we present the problem of designing dialog systems that enable lifelong learning as an important challenge problem, in particular for applications involving physically situated robots. We include examples of prior work in this direction, and discuss challenges that remain to be addressed.

PDF, Arxiv

In Position Paper Track at the SIGDIAL Special Session on Physically Situated Dialogue (RoboDial 2.0), July 2020.

@inproceedings{padmakumar:robodial20, title={Dialog as a Vehicle for Lifelong Learning}, author={Aishwarya Padmakumar and Raymond J. Mooney}, booktitle={Position Paper Track at the SIGDIAL Special Session on Physically Situated Dialogue (RoboDial 2.0)}, month={July}, url="http://www.cs.utexas.edu/users/ai-labpub-view.php?PubID=127835", year={2020} }

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