Bilateral personalized dialogue generation with contrastive learning

Bin Li, Hanjun Deng

Abstract: Generating personalized responses is one of the major challenges in natural human–robot interaction. Current studies 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 proposed method achieves the improvement on the random personalized test set of 23.99 in bilateral persona accuracy, 1.1 in BLEU, 0.83 in F1, 0.02 in distinct score, and the improvement on the biased personalized test set of 5.56 in bilateral persona accuracy, 7.51 in BLEU, 2.12 in F1, 0.02 in distinct score. On the manual evaluations, the proposed method can generate more fluency, bilateral persona-consistent, and context-consistent responses compared with other state-of-the-art methods.