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.

Accepted by ACL 2020, code is avaiable at https://github.com/SivilTaram/Persona-Dialogue-Generation

References in corpus (5)

• PyTorch: An Imperative Style, High-Performance Deep Learning Library
• Distilling the Knowledge in a Neural Network
• Sequence to Sequence Learning with Neural Networks
• TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agents
• The Optimal Reward Baseline for Gradient-Based Reinforcement Learning

Cited by in corpus (2)

• Generate, Delete and Rewrite: A Three-Stage Framework for Improving Persona Consistency of Dialogue Generation
• Improvement of a dedicated model for open domain persona-aware dialogue generation