Improving Multi-turn Emotional Support Dialogue Generation with Lookahead Strategy Planning

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 3014 - 3026 December 7-11, 2022 ©2022 Association for Computational Linguistics Improving Multi-turn Emotional Support Dialogue Generation with Lookahead Strategy Planning Yi Cheng1∗, Wenge Liu2∗, Wenjie Li1† , Jiashuo Wang1, Ruihui Zhao3, Bang Liu4‡ , Xiaodan Liang5, Yefeng Zheng3 1Hong Kong Polytechnic University 2Baidu Inc., Beijing, China 3Tencent Jarvis Lab 4RALI & Mila, Université de Montréal 5Sun Yat-sen University {csycheng,cswjli,csjwang}@comp.polyu.edu.hk, {kzllwg,xdliang328}@gmail.com, bang.liu@umontreal.ca, {zacharyzhao,yefengzheng}@tencent.com Abstract Providing Emotional Support (ES) to soothe people in emotional distress is an essential ca- pability in social interactions. Most existing research on building ES conversation systems only considers single-turn interactions with users, which is over-simplified. In compari- son, multi-turn ES conversation systems can provide ES more effectively, but face several new technical challenges, including: i) how to conduct support strategy planning that could lead to the best supporting effects; ii) how to dy- namically model the user’s state. In this paper, we propose a novel system named MultiESC to address these issues. For strategy planning, drawing inspiration from the A* search algo- rithm, we propose lookahead heuristics to esti- mate the future user feedback after using par- ticular strategies, which helps to select strate- gies that can lead to the best long-term effects. For user state modeling, MultiESC focuses on capturing users’ subtle emotional expressions and understanding their emotion causes. Ex- tensive experiments show that MultiESC sig- nificantly outperforms competitive baselines in both strategy planning and dialogue gen- eration. Our codes are available at https: //github.com/lwgkzl/MultiESC. 1 Introduction Almost every human has experienced emotional distress, even if not suffering from any mental dis- orders. Frequently, people deal with the distress by seeking Emotional Support (ES) from social interactions (Langford et al., 1997; Greene, 2003). Nevertheless, ES from family and friends is not al- ways available (Webber and Mascari, 2018). With the potential of providing more people with in-time support, developing Emotional Support Conversa- tion (ESC) systems has attracted much attention. However, since early ES datasets are constructed ∗Equal contribution. †Corresponding author. ‡Canada CIFAR AI Chair. Figure 1: An example of an emotional support conversa- tion between the support-seeker (left) and the supporter (right). The support strategies adopted by the supporter are presented in red italics before the utterances. by crawling post-response pairs from online fo- rums, they only contain single-turn conversations (Medeiros and Bosse, 2018; Sharma et al., 2020). Thus, most of the existing research on ESC also only considers single-turn interactions with the user (Medeiros and Bosse, 2018; Sharma et al., 2020, 2021), which is over-simplified and has limited support effects. It was not until recently that Liu et al. (2021) released the first large-scale multi-turn ES dataset, ESCONV. They also designed an ESC framework, suggesting the conversation procedures and support strategies for multi-turn ESC. Compared to the single-turn scenario, develop- ing multi-turn ESC systems faces several new chal- lenges. One significant challenge is support strat- egy planning. As pointed out in the psychologi- cal literature, particular procedures and strategies are indispensable …

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