Don't Lose Yourself! Empathetic Response Generation via Explicit Self-Other Awareness
This work addresses the challenge of creating more human-like empathetic chatbots, though it appears incremental by building on prior methods focused on other-awareness.
The paper tackles the problem of generating empathetic responses in chatbots by introducing explicit self-other awareness, which involves maintaining the system's own views alongside the user's, and demonstrates that their method produces more empathetic responses in evaluations.
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.