TransESC：通过轮次级状态转移平滑情感支持对话
TransESC: Smoothing Emotional Support Conversation via Turn-Level State Transition
AI总结：
针对情感支持对话话语过渡不流畅的问题，提出TransESC模型，通过“先转移后交互”的两步法构建三类轮次级状态转移图并注入解码器，实验证明其能生成更流畅有效的支持性回复。
摘要：
情感支持对话（Emotion Support Conversation，ESC）是一项新兴且具挑战性的任务，目标是减轻人们的情绪困扰。现有方法无法维持ESC中话语间的平滑过渡，因为它们未把握每个对话轮次的细粒度转移信息。为解决该问题，我们提出考虑ESC的轮次级状态转移（TransESC），从语义转移、策略转移和情感转移三个维度推动对话流畅自然地进行。具体而言，我们以“先转移后交互”的两步方式构建状态转移图，以捕捉这三类轮次级转移信息，最终将其注入具备转移感知的解码器以生成更具吸引力的回复。在基准数据集上的自动评估与人工评估均表明，TransESC在生成更流畅有效的支持性回复方面具备优势。我们的源代码可在https://github.com/circle-hit/TransESC 获取。
英文摘要：
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}.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
