You Impress Me: Dialogue Generation via Mutual Persona Perception

TL;DR

This paper introduces P^2 Bot, a dialogue system that explicitly models mutual understanding between interlocutors through persona perception, significantly improving personalized chit-chat quality.

Contribution

It proposes a novel transmitter-receiver framework incorporating mutual persona perception to enhance dialogue understanding and generation.

Findings

Outperforms state-of-the-art baselines on Persona-Chat dataset.

Shows significant improvements in automatic and human evaluation metrics.

Highlights the importance of modeling mutual understanding in dialogue systems.

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.