Commonsense-augmented Memory Construction and Management in Long-term Conversations via Context-aware Persona Refinement
Conference of the European Chapter of the Association for Computational Linguistics(2024)
摘要
Memorizing and utilizing speakers' personas is a common practice for response
generation in long-term conversations. Yet, human-authored datasets often
provide uninformative persona sentences that hinder response quality. This
paper presents a novel framework that leverages commonsense-based persona
expansion to address such issues in long-term conversation. While prior work
focuses on not producing personas that contradict others, we focus on
transforming contradictory personas into sentences that contain rich speaker
information, by refining them based on their contextual backgrounds with
designed strategies. As the pioneer of persona expansion in multi-session
settings, our framework facilitates better response generation via human-like
persona refinement. The supplementary video of our work is available at
https://caffeine-15bbf.web.app/.
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