You Impress Me: Dialogue Generation via Mutual Persona Perception
Abstract
Despite the continuing efforts to improve the engagingness and consistency of chit-chat dialogue systems, the majority of current work simply focus on mimicking human-like responses, leaving understudied the aspects of modeling understanding between interlocutors. The research in cognitive science, instead, suggests that understanding is an essential signal for a high-quality chit-chat conversation. Motivated by this, we propose P^2 Bot, a transmitter-receiver based framework with the aim of explicitly modeling understanding. Specifically, P^2 Bot incorporates mutual persona perception to enhance the quality of personalized dialogue generation. Experiments on a large public dataset, Persona-Chat, demonstrate the effectiveness of our approach, with a considerable boost over the state-of-the-art baselines across both automatic metrics and human evaluations.
- Publication:
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arXiv e-prints
- Pub Date:
- April 2020
- DOI:
- 10.48550/arXiv.2004.05388
- arXiv:
- arXiv:2004.05388
- Bibcode:
- 2020arXiv200405388L
- Keywords:
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- Computer Science - Computation and Language;
- Computer Science - Artificial Intelligence
- E-Print:
- Accepted by ACL 2020, code is avaiable at https://github.com/SivilTaram/Persona-Dialogue-Generation