Empathetic Dialogue Generation via Sentiment and Support Strategy

ZHU Zhenfang; LI Jiaxin; XU Fuyong; LIU Peiyu; ZHANG Guangyuan

doi:10.11999/JEIT231417

Volume 46 Issue 8

Aug. 2024

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Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(8): 3382-3389

ZHU Zhenfang, LI Jiaxin, XU Fuyong, LIU Peiyu, ZHANG Guangyuan. Empathetic Dialogue Generation via Sentiment and Support Strategy[J]. Journal of Electronics & Information Technology, 2024, 46(8): 3382-3389. doi: 10.11999/JEIT231417

Citation:

ZHU Zhenfang, LI Jiaxin, XU Fuyong, LIU Peiyu, ZHANG Guangyuan. Empathetic Dialogue Generation via Sentiment and Support Strategy[J]. Journal of Electronics & Information Technology, 2024, 46(8): 3382-3389. doi: 10.11999/JEIT231417

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Empathetic Dialogue Generation via Sentiment and Support Strategy

doi: 10.11999/JEIT231417 cstr: 32379.14.JEIT231417

1.
School of Information Science and Electrical Engineering, Shandong Jiaotong University, Jinan 250357, China
2.
School of Information Science and Engineering, Shandong Normal University, Jinan 250358, China

Funds: The National Social Science Foundation (19BYY076)

Received Date: 2023-12-25
Rev Recd Date: 2024-04-23

Available Online: 2024-07-25

Publish Date: 2024-08-10

Abstract

Abstract

Empathic dialogue aims to provide mental health support for anxious users, thus chatbots with empathic capabilities is a noteworthy issue. The existing methods can only identify users’ sentiment states, but can not effectively generate empathetic responses according to users’ different sentiment states and let alone effectively relieve users’ bad emotions. Therefore, in the research of building sentiment support chatbots, how to dynamically capture users’ fine-grained sentiment features and provide corresponding psychological support according to sentiment features needs to be further explored. This paper proposes an empathic dialogue generation method based on the fusion of emotion and strategy. Firstly, the sentiment classification network is used to dynamically perceive the user’s sentiment state. Then the support strategy is used to accurately model the strategy matching network which is introduced according to the context of the conversation to generate the conversation. Finally, by comparing the experimental results of the proposed method and the current advanced methods on the corresponding datasets, the effectiveness of the proposed method and the importance of sentiment support are verified.
- Empathic dialogue,
- Sentiment support,
- Dialogue generation,
- Support strategy

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References(22)

References

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