MISC : A Mixed Strategy-Aware Model integrating COMET for Emotional Support Conversation (2022)

Abstract: Applying existing methods to emotional support conversation—which provides valuable assistance to people who are in need—has two major limitations: (a) they generally employ a conversation-level emotion label, which is too coarse-grained to capture user’s instant mental state; (b) most of them focus on expressing empathy in the response(s) rather than gradually reducing user’s distress. To address the problems, we propose a novel model \textbf{MISC} , which firstly infers the user’s fine-grained emotional status, and then responds skillfully using a mixture of strategy. Experimental results on the benchmark dataset demonstrate the effectiveness of our method and reveal the benefits of fine-grained emotion understanding as well as mixed-up strategy modeling.

## introduction empathy is the ability to perceive what others feel, think in their places and respond properly. it has a broad application scenarios to endow machines with the ability of empathy, including automatic psycho-therapist, intelligent customer service, empathetic conversational agents, and etc @xcite @xcite . in this work, we focus on a special kind of human-computer empathetic conversation, i.e., emotional support conversation @xcite . distinguishedly, emotional support conversation happens between a seeker and supporter, where the supporter aims to gradually reduce seeker's distress as the conversation goes. this makes existing approaches unsuitable for our setting for at least two reasons. firstly, existing work on emotional chatting learns to predict user emotion using a conversation-level emotion label, which is coarse-grained and static to the conversation context @xcite @xcite . however, emotion is complex and user emotion intensity will change during the developing of the conversation @xcite . it is thus a necessity to tell seeker's fine-grained mental state at each utterance. secondly, most of empathetic chatbots are trained to respond emotionally in accordance with the predicted coarse-grained emotion class, without consideration on how to address the seeker's emotional problem @xcite @xcite . hence, they are deficient to apply for emotional support conversation whose goal is to help others work through the challenges they face. to tackle these issues, we propose a novel approach misc, a.k.a. mixed srategy-aware model integrating comet for emotional support conversation. as to the first issue, we introduce comet, a pre-trained generative commonsense reasoning model @xcite , and devise an attention mechanism to selectively adopt the comet knowledge tuples for fine-grained emotion understanding. as shown in figure 1 , this allows us to capture seeker's instantaneous mental state using different comet tuples. in addition, we propose to also consider response strategy when generating empathetic responses for the second issue. instead of modeling response strategy as a one-hot indi-cator, we formulate it as a probability distribution over a strategy codebook, and guide the response generation using a mixture of strategies. at last, our misc produces supportive responses based on both comet-enhanced mental information and distributed strategy representation. the unique design of mixed strategy not only helps to increase the expressed empathy, but also facilitates to learn the gradual transition in the long response, as the last utterance in figure 1 , which will in turn make the conversation more smooth. to evaluate our model, we conduct extensive experiments on esconv benchmark @xcite and compare with 5 state-of-the-art empathetic chatbots. based on both automatic metrics and manual judgments, we demonstrate that the responses generated by our model misc are more relevant and empathetic. besides, additional experimental analysis reveal the importance of response strategy modeling, and sheds light on how to learn a proper response strategy as well as how response strategy could influence the empathy of the chatbot. ## model: misc the overview of our approach is shown in figure 2 . based on blenderbot-small @xcite , our model misc consists three main components: (1) a mental state-enhanced encoder @xcite ; (2) a mixed strategy learning module; and ## analysis our method misc has two novel designs: considering the fine-grained mental states and incorporating a mixture of response strategy. to investigate more, we conduct extra experiments, and the analysis results give us hints of how to develop better emotional support conversational agents. ## conclusions in this paper, we propose misc, a novel framework for emotional support conversation, which introduces comet to capture user's instant mental state, and devises a mixed strategy-aware decoder to generate supportive response. through extensive experiments, we prove the superiority and rationality of our model. in the future, we plan to learn the mixed response strategy in a dynamic way.