PersonaCLR: Evaluation Model for Persona Characteristics via Contrastive Learning of Linguistic Style Representation
Michimasa Inaba · 2024
Persona-aware dialogue systems can improve the consistency of the system's responses, users' trust and user enjoyment.Filtering nonpersona-like utterances is important for constructing persona-aware dialogue systems.This paper presents the PersonaCLR model for capturing a given utterance's intensity of persona characteristics.We trained the model with contrastive learning based on the sameness of the utterances' speaker.Contrastive learning enables PersonaCLR to evaluate the persona characteristics of a given utterance, even if the target persona is not included in training data.For training and evaluating our model, we also constructed a new dataset of 2,155 character utterances from 100 Japanese online novels.Experimental results indicated that our model outperforms existing methods and a strong baseline using a large language model.Our source code, pre-trained model, and dataset are available at https://github.com/1never/PersonaCLR.