Towards Emotional Support Dialog Systems (2021)

Abstract: Emotional support is a crucial ability for many conversation scenarios, including social interactions, mental health support, and customer service chats. Following reasonable procedures and using various support skills can help to effectively provide support. However, due to the lack of a well-designed task and corpora of effective emotional support conversations, research on building emotional support into dialog systems remains lacking. In this paper, we define the Emotional Support Conversation (ESC) task and propose an ESC Framework, which is grounded on the Helping Skills Theory. We construct an Emotion Support Conversation dataset (ESConv) with rich annotation (especially support strategy) in a help-seeker and supporter mode. To ensure a corpus of high-quality conversations that provide examples of effective emotional support, we take extensive effort to design training tutorials for supporters and several mechanisms for quality control during data collection. Finally, we evaluate state-of-the-art dialog models with respect to the ability to provide emotional support. Our results show the importance of support strategies in providing effective emotional support and the utility of ESConv in training more emotional support systems.

## introduction emotional support (es) aims at reducing individuals' emotional distress and helping them understand and work through the challenges that they face @xcite @xcite . it is a critical capacity to train into dialog systems that interact with users 😿 i feel so frustrated. ## data collection to facilitate the research of emotional support skills in dialog systems, we introduce an emotional support conversation dataset, esconv, which is collected in a help-seeker and supporter mode with crowdworkers. as high-quality conversation examples are needed for this complex task, we took tremendous effort to try to ensure the effectiveness of es in conversations. our efforts included the following major aspects: (1) because providing conversational support is a skill that must be trained for supporters to be effective @xcite , we design a tutorial with the esc framework and train crowdworkers to be supporters. only those who pass the examination are admitted to the task. (2) we require help-seekers to complete a pre-chat survey on their problems and emotions and to provide feedback during and after the conversations. (3) we devise and use multiple manual or automatic mechanisms to filter out the low-quality conversations after collecting raw dialog data. ## experiments our experiments focus on two key questions: (1) how much can esconv with strategy annotation improve state-of-the-art generative dialog models? (2) can these models learn to provide effective emotional support from esconv? 6.1 backbone models we used two state-of-the-art pre-trained models as the backbones of the compared variant models: blenderbot blenderbot @xcite ) is an open-domain conversational agent trained with multiple communication skills, including empathetic responding. as such, blenderbot should be capable of providing es for users to some extent. we used the small version foot_1 of blenderbot in experiments, because the larger versions have the limitation of maximum context length 128, which we found harms the model performance and response coherence. dialogpt we additionally evaluated dialogpt @xcite , which is a gpt-2-based model pre-trained on large-scale dialog corpora. we used the small version 4 . ## conclusion in this work, we define the task of emotional support conversation and present an esc framework. the esc framework is adapted from the helping skills theory into a dialog system setting, which characterizes three stages with corresponding support strategies useful at each stage. we then construct an emotional support conversation dataset, esconv. we carefully design the process of data collection and devise multiple mechanisms to ensure the effectiveness of es in conversations. finally, we evaluate the es ability with state-of-theart dialog models. experimental results show the potential utility of esconv in terms of improving dialog systems' ability to provide effective es. our work can facilitate future research of es dialog systems, as well as improve models for other conversation scenarios where emotional support plays an important role. strategy selection and realization, user state modeling, and task evaluation are important directions for further research.