TransESC: Smoothing Emotional Support Conversation via Turn-Level State
  Transition
Weixiang Zhao Yanyan Zhao Shilong Wang Bing Qin
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 ignore to
grasp the fine-grained transition information at each dialogue turn. To solve
this problem, we propose to take into account turn-level state
\textbf{Trans}itions of \textbf{ESC} (\textbf{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 is available at
\url{https://github.com/circle-hit/TransESC}.