Towards Emotional Support Dialog Systems

TL;DR

This paper introduces the ESC task and dataset, enabling the development of dialog systems capable of providing effective emotional support, which was previously hindered by lack of suitable data and frameworks.

Contribution

It defines the ESC task, proposes a new framework based on Helping Skills Theory, and constructs the ESConv dataset with high-quality annotations for training emotional support dialog systems.

Findings

Support strategies improve emotional support effectiveness

ESConv dataset facilitates training better support models

State-of-the-art models show varied support capabilities

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 untouched. 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.