@inproceedings{inaba-2024-personaclr,
title = "{P}ersona{CLR}: Evaluation Model for Persona Characteristics via Contrastive Learning of Linguistic Style Representation",
author = "Inaba, Michimasa",
editor = "Kawahara, Tatsuya and
Demberg, Vera and
Ultes, Stefan and
Inoue, Koji and
Mehri, Shikib and
Howcroft, David and
Komatani, Kazunori",
booktitle = "Proceedings of the 25th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
month = sep,
year = "2024",
address = "Kyoto, Japan",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.sigdial-1.58",
pages = "674--685",
abstract = "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.",
}
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<abstract>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.</abstract>
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%0 Conference Proceedings
%T PersonaCLR: Evaluation Model for Persona Characteristics via Contrastive Learning of Linguistic Style Representation
%A Inaba, Michimasa
%Y Kawahara, Tatsuya
%Y Demberg, Vera
%Y Ultes, Stefan
%Y Inoue, Koji
%Y Mehri, Shikib
%Y Howcroft, David
%Y Komatani, Kazunori
%S Proceedings of the 25th Annual Meeting of the Special Interest Group on Discourse and Dialogue
%D 2024
%8 September
%I Association for Computational Linguistics
%C Kyoto, Japan
%F inaba-2024-personaclr
%X 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.
%U https://aclanthology.org/2024.sigdial-1.58
%P 674-685
Markdown (Informal)
[PersonaCLR: Evaluation Model for Persona Characteristics via Contrastive Learning of Linguistic Style Representation](https://aclanthology.org/2024.sigdial-1.58) (Inaba, SIGDIAL 2024)
ACL