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        <title>Interactive Learning of Grounded Verb Semantics towards Human-Robot Communication</title>
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            <namePart type="family">Barzilay</namePart>
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            <namePart type="given">Min-Yen</namePart>
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    <abstract>To enable human-robot communication and collaboration, previous works represent grounded verb semantics as the potential change of state to the physical world caused by these verbs. Grounded verb semantics are acquired mainly based on the parallel data of the use of a verb phrase and its corresponding sequences of primitive actions demonstrated by humans. The rich interaction between teachers and students that is considered important in learning new skills has not yet been explored. To address this limitation, this paper presents a new interactive learning approach that allows robots to proactively engage in interaction with human partners by asking good questions to learn models for grounded verb semantics. The proposed approach uses reinforcement learning to allow the robot to acquire an optimal policy for its question-asking behaviors by maximizing the long-term reward. Our empirical results have shown that the interactive learning approach leads to more reliable models for grounded verb semantics, especially in the noisy environment which is full of uncertainties. Compared to previous work, the models acquired from interactive learning result in a 48% to 145% performance gain when applied in new situations.</abstract>
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%0 Conference Proceedings
%T Interactive Learning of Grounded Verb Semantics towards Human-Robot Communication
%A She, Lanbo
%A Chai, Joyce
%Y Barzilay, Regina
%Y Kan, Min-Yen
%S Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2017
%8 July
%I Association for Computational Linguistics
%C Vancouver, Canada
%F she-chai-2017-interactive
%X To enable human-robot communication and collaboration, previous works represent grounded verb semantics as the potential change of state to the physical world caused by these verbs. Grounded verb semantics are acquired mainly based on the parallel data of the use of a verb phrase and its corresponding sequences of primitive actions demonstrated by humans. The rich interaction between teachers and students that is considered important in learning new skills has not yet been explored. To address this limitation, this paper presents a new interactive learning approach that allows robots to proactively engage in interaction with human partners by asking good questions to learn models for grounded verb semantics. The proposed approach uses reinforcement learning to allow the robot to acquire an optimal policy for its question-asking behaviors by maximizing the long-term reward. Our empirical results have shown that the interactive learning approach leads to more reliable models for grounded verb semantics, especially in the noisy environment which is full of uncertainties. Compared to previous work, the models acquired from interactive learning result in a 48% to 145% performance gain when applied in new situations.
%R 10.18653/v1/P17-1150
%U https://aclanthology.org/P17-1150
%U https://doi.org/10.18653/v1/P17-1150
%P 1634-1644

Markdown (Informal)

[Interactive Learning of Grounded Verb Semantics towards Human-Robot Communication](https://aclanthology.org/P17-1150) (She & Chai, ACL 2017)

• Interactive Learning of Grounded Verb Semantics towards Human-Robot Communication (She & Chai, ACL 2017)

ACL

• Lanbo She and Joyce Chai. 2017. Interactive Learning of Grounded Verb Semantics towards Human-Robot Communication. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1634–1644, Vancouver, Canada. Association for Computational Linguistics.