  Providing emotional support through dialogue systems is becoming increasingly
important in today's world, as it can support both mental health and social
interactions in many conversation scenarios. Previous works have shown that
using persona is effective for generating empathetic and supportive responses.
They have often relied on pre-provided persona rather than inferring them
during conversations. However, it is not always possible to obtain a user
persona before the conversation begins. To address this challenge, we propose
PESS (Persona Extraction through Semantic Similarity), a novel framework that
can automatically infer informative and consistent persona from dialogues. We
devise completeness loss and consistency loss based on semantic similarity
scores. The completeness loss encourages the model to generate missing persona
information, and the consistency loss guides the model to distinguish between
consistent and inconsistent persona. Our experimental results demonstrate that
high-quality persona information inferred by PESS is effective in generating
emotionally supportive responses.
