Bilateral Personalized Dialogue Generation with Contrastive Learning

TL;DR

This paper introduces a bilateral personalized dialogue generation method that incorporates both user and robot personas using contrastive learning and a dynamic fusion approach, improving personalization and consistency in responses.

Contribution

It presents a novel bilateral personalized dialogue generation framework with a dynamic persona-aware fusion and a contrastive learning-based response selection, addressing limitations of previous unilateral approaches.

Findings

Outperforms state-of-the-art methods in personalization accuracy.

Enhances response consistency with bilateral personas.

Improves automatic and manual evaluation scores.

Abstract

Generating personalized responses is one of the major challenges in natural human-robot interaction. Current researches in this field mainly focus on generating responses consistent with the robot's pre-assigned persona, while ignoring the user's persona. Such responses may be inappropriate or even offensive, which may lead to the bad user experience. Therefore, we propose a Bilateral Personalized Dialogue Generation (BPDG) method for dyadic conversation, which integrates user and robot personas into dialogue generation via designing a dynamic persona-aware fusion method. To bridge the gap between the learning objective function and evaluation metrics, the Conditional Mutual Information Maximum (CMIM) criterion is adopted with contrastive learning to select the proper response from the generated candidates. Moreover, a bilateral persona accuracy metric is designed to measure the degree of bilateral personalization. Experimental results demonstrate that, compared with several state-of-the-art methods, the final results of the proposed method are more personalized and consistent with bilateral personas in terms of both automatic and manual evaluations.