Emotional Chatting Machine: Emotional Conversation Generation with Internal and External Memory

Abstract

Perception and expression of emotion are key factors to the success of dialogue systems or conversational agents. However, this problem has not been studied in large-scale conversation generation so far. In this paper, we propose Emotional Chatting Machine (ECM) that can generate appropriate responses not only in content (relevant and grammatical) but also in emotion (emotionally consistent). To the best of our knowledge, this is the first work that addresses the emotion factor in large-scale conversation generation. ECM addresses the factor using three new mechanisms that respectively (1) models the high-level abstraction of emotion expressions by embedding emotion categories, (2) captures the change of implicit internal emotion states, and (3) uses explicit emotion expressions with an external emotion vocabulary. Experiments show that the proposed model can generate responses appropriate not only in content but also in emotion.

Knowledge Bridging for Empathetic Dialogue Generation

“…To evaluate the model at the emotional level, we adopt Emotion Accuracy as the agreement between the ground truth emotion labels and the predicted emotion labels. Following previous emotion-related studies (Zhou et al 2018a;Rashkin et al 2019;Song et al 2019;Wei et al 2019;Li et al 2020) Human Evaluations. We randomly sample 100 dialogues and their corresponding generations from KEMP as well as the baselines.…”

“…With the rise of data-driven learning approaches (Sutskever, Vinyals, and Le 2014;Vaswani et al 2017), open-domain dialogue generation models have seen growing interests in recent years (Vinyals and Le 2015;Shang, Lu, and Li 2015;Serban et al 2016;Li et al 2016b;Zhou et al 2018b;Dinan et al 2019). To control the emotional content of the target output, recent approaches generate emotional responses conditioning on a manually specified label (Zhou et al 2018a;Li and Sun 2018;Zhou and Wang 2018;Huang et al 2018;Wei et al 2019;Colombo et al 2019;Shen and Feng 2020). However, existing emotional dialogue models purely focus on whether the generated response matches a predetermined emotion, whereas in real-world scenarios the listener is capable to infer the emotion of the speaker (Rashkin et al 2019).…”

“…Studies on social psychology suggest that empathy is a crucial factor towards a more humanized dialogue system (Zech and Rimé 2005). Although plenty of researchers have attempted to control the emotional content of response either through an explicitly assigned emotional label (Zhou and Wang 2018;Zhou et al 2018a;Wang and Wan 2018;Song et al 2019;Shen and Feng 2020) or through a general term to encourage higher levels of affect (Asghar et al 2018), it is still challenging for chatbots to conduct empathetic dialogues without the explicit emotion labels (empathetic dialogue problem) (Zhou et al 2018a;Rashkin et al 2019). Several recent works have been proposed to address the empathetic dialogue problem based on multi-task learning (Rashkin et al 2018(Rashkin et al , 2019Wei et al 2019;Lin et al 2020), the mixture of experts (Lin et al 2019), emotion mimicry (Majumder et al 2020), or multi-resolution user feedback (Li et al 2020).…”

A survey on deep reinforcement learning for audio-based applications

“…In this way, the authors found that the proposed method improves speech recognition performance. The ASR system of Zhou et al (2018) was jointly trained with maximum likelihood and policy gradient to improve via end-to-end learning. They were able to optimise the performance metric directly with policy learning and achieve 4% to 13% relative improvement for end-to-end ASR.…”

“…Arguably, human-robot interaction can be significantly enhanced if dialogue agents can perceive the emotional state of a user and its dynamics (Ma et al 2020;Majumder et al 2019). This line of research is categorised into two areas: emotion recognition in conversations , and affective dialogue generation (Young et al 2020;Zhou et al 2018). Speech emotion recognition (SER) can be used as a reward for RL based dialogue systems (Heusser et al 2019).…”