Abstract
Emotional support conversation aims to provide comfort and suggestions to users and gradually reduce their negative emotions such as anxiety. It is a valuable topic for many applications, including mental health support and customer service chats. However, due to the lack of enough expert knowledge, existing methods fail to provide enlightened suggestions to reverse users’ worries. Additionally, these methods neglect to grasp the mental state evolution of users. To address these problems, we propose a novel method that considers Mental State Evolution to provide Knowledge-grounded Suggestions (MEKS). In detail, we first create a suggestion corpus called MentalQA to grasp the psychological knowledge by resorting to the mental health forum. The relevant passages are selected based on both the context and the original response. Then we leverage graph structure to enrich the context with the inferred user’s mental state evolution. Furthermore, we introduce a gate to combine textual expert knowledge with the mental state evolution graph, so as to facilitate the generation of supportive responses. Experimental results show that this method can provide reasonable solutions to help the users.
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Acknowledgement
This work is supported by the National Natural Science Foundation of China (62276279), Key-Area Research and Development Program of Guangdong Province (2020B0101100001), the Tencent WeChat Rhino-Bird Focused Research Program (WXG-FR-2023-06), and Zhuhai Industry-University-Research Cooperation Project (2220004002549). Jianxing Yu is the corresponding author.
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Gan, M., Yu, J., Dong, X., Qiu, S., Liu, W., Yin, J. (2023). Generating Enlightened Suggestions Based on Mental State Evolution for Emotional Support Conversation. In: Yang, X., et al. Advanced Data Mining and Applications. ADMA 2023. Lecture Notes in Computer Science(), vol 14176. Springer, Cham. https://doi.org/10.1007/978-3-031-46661-8_22
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