不要迷失自我！通过显式自我-他人感知生成共情回复
Don't Lose Yourself! Empathetic Response Generation via Explicit Self-Other Awareness
AI总结：
针对现有共情回复生成方法仅关注他人感知、忽略系统自身观点的问题，该研究提出带显式自我-他人感知的方法EmpSOA，经多阶段处理注入相关信息，在基准测试中表现更优。
摘要：
作为实现类人聊天机器人的关键一步，共情回复生成受到了越来越多的关注。以往的研究存在局限性，不足以充分引发共情，因为它们仅关注共情的初始层面，通过他人感知自动模仿用户的感受和想法，却忽略了维持并考虑系统自身观点，而这是实现共情的关键过程，即自我-他人感知。为此，我们提出了带有显式自我-他人感知的共情回复生成方法EmpSOA。具体而言，我们设计了自我-他人区分、自我-他人调节、自我-他人生成三个阶段，将自我-他人感知信息清晰地维持、调控并注入共情回复生成过程中。在基准数据集上的自动评估和人工评估均表明，EmpSOA在生成更具共情性的回复方面具有优势。
英文摘要：
As a critical step to achieve human-like chatbots, empathetic response generation has attained increasing interests. Previous attempts are incomplete and not sufficient enough to elicit empathy because they only focus on the initial aspect of empathy to automatically mimic the feelings and thoughts of the user via other-awareness. However, they ignore to maintain and take the own views of the system into account, which is a crucial process to achieve the empathy called self-other awareness. To this end, we propose to generate Empathetic response with explicit Self-Other Awareness (EmpSOA). Specifically, three stages, self-other differentiation, self-other modulation and self-other generation, are devised to clearly maintain, regulate and inject the self-other aware information into the process of empathetic response generation. Both automatic and human evaluations on the benchmark dataset demonstrate the superiority of EmpSOA to generate more empathetic responses.
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。
