T rans ESC : Smoothing Emotional Support Conversation via Turn-Level State Transition (2023)

Abstract: Emotion Support Conversation (ESC) is an emerging and challenging task with the goal of reducing the emotional distress of people. Previous attempts fail to maintain smooth transitions between utterances in ESC because they ignoring to grasp the fine-grained transition information at each dialogue turn. To solve this problem, we propose to take into account turn-level state Transitions of ESC (TransESC) from three perspectives, including semantics transition, strategy transition and emotion transition, to drive the conversation in a smooth and natural way. Specifically, we construct the state transition graph with a two-step way, named transit-then-interact, to grasp such three types of turn-level transition information. Finally, they are injected into the transition aware decoder to generate more engaging responses. Both automatic and human evaluations on the benchmark dataset demonstrate the superiority of TransESC to generate more smooth and effective supportive responses. Our source code will be publicly available.

## introduction emotional support conversation (esc) is a goaldirected task which aims at reducing individuals' emotional distress and bringing about modifications in the psychological states of them. it is a desirable and critical capacity that an engaging chatbot is expected to have and has potential applications in several areas such as mental health support, customer service platform, etc. different from the emotional @xcite ) and empathetic @xcite conversation, esc is always of long turns, which requires skillful conversation procedures and support strategies to achieve the goal. for example, as shown in figure 1 , the supporter should firstly explore the situation to identify the problems faced by the seeker, and i really agree with you. i am worth more than that. it is time to move on, even when it is difficult. i am really sad as my partner walked out on me. ## methodology the overall architecture of our proposed transesc is shown in figure 2 . the dialogue representations are first obtained through context encoder. then we grasp and propagate the fine-grained transition information, including semantics transition, strategy transition and emotion transition, in the turn-level state transition module. finally, to generate more natural and smooth emotional support responses, such transition information is clearly injected into the transition-aware decoder. ## conclusion and future work in this paper, we propose transesc to generate emotional support via turn-level state transition information incorporated, including semantics transition, strategy transition and emotion transition. we construct the transition graph with the two-step way, transit-then-interact, to grasp and supervise the transition information at each dialogue turn. experimental results on both automatic and human evaluation demonstrate the superiority of trans-esc to generate more smooth responses. in the future, we will explore more characteristics in esc such as persona to generate more natural responses.