EmpDG: Multi-resolution Interactive Empathetic Dialogue Generation

Proceedings of the 28th International Conference on Computational Linguistics, pages 4454–4466 Barcelona, Spain (Online), December 8-13, 2020 4454 EmpDG: Multi-resolution Interactive Empathetic Dialogue Generation Qintong Li1, Hongshen Chen2, Zhaochun Ren1,∗, Pengjie Ren1, Zhaopeng Tu3,Zhumin Chen1 1School of Computer Science and Technology, Shandong University, Qingdao, China 2Data Science Lab, JD.COM, Beijing, China 3Tencent AI Lab, Shenzhen, China qintongli@mail.sdu.edu.cn, ac@chenhongshen.com, {zhaochun.ren, chenzhumin}@sdu.edu.cn, jay.ren@outlook.com, zptu@tencent.com Abstract A humanized dialogue system is expected to generate empathetic replies, which should be sensitive to the users’ expressed emotion. The task of empathetic dialogue generation is proposed to address this problem. The essential challenges lie in accurately capturing the nuances of human emotion and considering the potential of user feedback, which are overlooked by the majority of existing work. In response to this problem, we propose a multi-resolution adversarial model – EmpDG, to generate more empathetic responses. EmpDG exploits both the coarse-grained dialogue-level and fine-grained token-level emotions, the latter of which helps to better capture the nuances of user emotion. In addition, we introduce an interactive adversarial learning framework which exploits the user feedback, to identify whether the generated responses evoke emotion perceptivity in dialogues. Experimental results show that the proposed approach significantly outperforms the state-of-the-art baselines in both content quality and emotion perceptivity. 1 Introduction Studies on social psychology suggest that “empathy” is a crucial step towards a more humanized human- machine conversation, which improves the emotional perceptivity in emotion-bonding social activi- ties (Zech and Rim´e, 2005). To design an intelligent automatic dialogue system, it is important to make the chatbot become empathetic within dialogue interactions (Prendinger and Ishizuka, 2005). Therefore, in this paper, we focus on the task of empathetic dialogue generation (Rashkin et al., 2019), which automatically tracks and understands the user emotion information in a multi-turn dialogue scenario. Despite the achieved successes (Rashkin et al., 2019; Lin et al., 2019), obstacles to establishing an empathetic conversational system are still far beyond current progress: (1) It is still difficult to accurately capture the nuances of human emotion in dialogue generation (Ghosal et al., 2019). (2) Merely relying on the dialogue history but overlooking the potential of user feedback for the generated responses further aggravates the aforementioned deficiencies, which causes undesirable responses (Zhang et al., 2018a). In Figure 1, we give an example of the benchmark dataset EMPATHETICDIALOGUES (Rashkin et al., 2019). Note that the emotional words in the sequence of utterances (in Figure 1, they are “new”, “job” in utterance 1, “amazing”, “excited” in utterance 2, and “excited” in utterance 3) have fine-grained emotional connections. Without considering fine-grained emotional words, the responses generated by existing methods are trivial and uninformed, even though they expressed the appropriate emotions. Therefore, explicitly modelling the fine-grained emotional factor is necessary. In this paper, we propose a multi-resolution adversarial empathetic dialogue generation model, named EmpDG, to address above challenges through generating more appropriate and empathetic responses. To capture the nuances of user emotions sufficiently, EmpDG generates responses by jointly taking coarse- grained dialogue-level emotions and fine-grained token-level emotions into …

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