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}.

翻译：情感支持对话（ESC）是一项新兴且富有挑战性的任务，旨在减轻人们的情绪困扰。现有方法因未能捕捉每个对话轮次中的细粒度转换信息，难以维持对话语句间的平滑过渡。为解决该问题，我们提出从语义转换、策略转换和情感转换三个维度建模情感支持对话的轮级状态转换（TransESC），以驱动对话以平滑自然的方式进行。具体而言，我们采用"先转换后交互"的两步法构建状态转换图，以获取这三类轮级转换信息。最后，将这些信息注入转换感知解码器，生成更具参与度的回复。在基准数据集上的自动评估和人工评估均表明，TransESC能生成更平滑且有效的支持性回复，显著优于现有方法。我们的源代码发布于\url{https://github.com/circle-hit/TransESC}。