Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/152720
Title: CARE : commonsense-aware emotional response generation with latent concepts
Authors: Zhang, Peixiang
Wang, Di
Li, Pengfei
Zhang, Chen
Wang, Hao
Miao, Chunyan
Keywords: Engineering::Computer science and engineering
Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Issue Date: 2021
Source: Zhang, P., Wang, D., Li, P., Zhang, C., Wang, H. & Miao, C. (2021). CARE : commonsense-aware emotional response generation with latent concepts. Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21), 35.
Project: Alibaba-NTU-AIR2019B1
AISG-GC-2019-003
NRF-NRFI05- 2019-0002
MOH/NIC/COG04/2017
MOH/NIC/HAIG03/2017
Abstract: Rationality and emotion are two fundamental elements of humans. Endowing agents with rationality and emotion has been one of the major milestones in AI. However, in the field of conversational AI, most existing models only specialize in one aspect and neglect the other, which often leads to dull or unrelated responses. In this paper, we hypothesize that combining rationality and emotion into conversational agents can improve response quality. To test the hypothesis, we focus on one fundamental aspect of rationality, i.e., commonsense, and propose CARE, a novel model for commonsense-aware emotional response generation. Specifically, we first propose a framework to learn and construct commonsense-aware emotional latent concepts of the response given an input message and a desired emotion. We then propose three methods to collaboratively incorporate the latent concepts into response generation. Experimental results on two large-scale datasets support our hypothesis and show that our model can produce more accurate and commonsense-aware emotional responses and achieve better human ratings than state-of-the-art models that only specialize in one aspect.
URI: https://hdl.handle.net/10356/152720
ISBN: 978-1-57735-866-4
ISSN: 2159-5399
Rights: © 2021 Association for the Advancement of Artificial Intelligence. All Rights Reserved. This paper was published in Proceedings of Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21) and is made available with permission of Association for the Advancement of Artificial Intelligence.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Conference Papers

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