@inproceedings{kim-etal-2023-persona,
title = "Persona Expansion with Commonsense Knowledge for Diverse and Consistent Response Generation",
author = "Kim, Donghyun and
Ahn, Youbin and
Kim, Wongyu and
Lee, Chanhee and
Lee, Kyungchan and
Lee, Kyong-Ho and
Kim, Jeonguk and
Shin, Donghoon and
Lee, Yeonsoo",
editor = "Vlachos, Andreas and
Augenstein, Isabelle",
booktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.eacl-main.81",
doi = "10.18653/v1/2023.eacl-main.81",
pages = "1139--1149",
abstract = "Generating diverse and consistent responses is the ultimate goal of a persona-based dialogue. Although many studies have been conducted, the generated responses tend to be generic and bland due to the personas{'} limited descriptiveness. Therefore, it is necessary to expand the given personas for more attractive responses. However, indiscriminate expansion of personas threaten the consistency of responses and therefore reduce the interlocutor{'}s interest in conversation. To alleviate this issue, we propose a consistent persona expansion framework that improves not only the diversity but also the consistency of persona-based responses. To do so, we define consistency criteria to avoid possible contradictions among personas as follows: 1) Intra-Consistency and 2) Inter-Consistency. Then, we construct a silver profile dataset to deliver the ability to conform with the consistency criteria to the expansion model. Finally, we propose a persona expansion model with an encoder-decoder structure, which considers the relatedness and consistency among personas. Our experiments on the Persona-Chat dataset demonstrate the superiority of the proposed framework.",
}
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<abstract>Generating diverse and consistent responses is the ultimate goal of a persona-based dialogue. Although many studies have been conducted, the generated responses tend to be generic and bland due to the personas’ limited descriptiveness. Therefore, it is necessary to expand the given personas for more attractive responses. However, indiscriminate expansion of personas threaten the consistency of responses and therefore reduce the interlocutor’s interest in conversation. To alleviate this issue, we propose a consistent persona expansion framework that improves not only the diversity but also the consistency of persona-based responses. To do so, we define consistency criteria to avoid possible contradictions among personas as follows: 1) Intra-Consistency and 2) Inter-Consistency. Then, we construct a silver profile dataset to deliver the ability to conform with the consistency criteria to the expansion model. Finally, we propose a persona expansion model with an encoder-decoder structure, which considers the relatedness and consistency among personas. Our experiments on the Persona-Chat dataset demonstrate the superiority of the proposed framework.</abstract>
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%0 Conference Proceedings
%T Persona Expansion with Commonsense Knowledge for Diverse and Consistent Response Generation
%A Kim, Donghyun
%A Ahn, Youbin
%A Kim, Wongyu
%A Lee, Chanhee
%A Lee, Kyungchan
%A Lee, Kyong-Ho
%A Kim, Jeonguk
%A Shin, Donghoon
%A Lee, Yeonsoo
%Y Vlachos, Andreas
%Y Augenstein, Isabelle
%S Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F kim-etal-2023-persona
%X Generating diverse and consistent responses is the ultimate goal of a persona-based dialogue. Although many studies have been conducted, the generated responses tend to be generic and bland due to the personas’ limited descriptiveness. Therefore, it is necessary to expand the given personas for more attractive responses. However, indiscriminate expansion of personas threaten the consistency of responses and therefore reduce the interlocutor’s interest in conversation. To alleviate this issue, we propose a consistent persona expansion framework that improves not only the diversity but also the consistency of persona-based responses. To do so, we define consistency criteria to avoid possible contradictions among personas as follows: 1) Intra-Consistency and 2) Inter-Consistency. Then, we construct a silver profile dataset to deliver the ability to conform with the consistency criteria to the expansion model. Finally, we propose a persona expansion model with an encoder-decoder structure, which considers the relatedness and consistency among personas. Our experiments on the Persona-Chat dataset demonstrate the superiority of the proposed framework.
%R 10.18653/v1/2023.eacl-main.81
%U https://aclanthology.org/2023.eacl-main.81
%U https://doi.org/10.18653/v1/2023.eacl-main.81
%P 1139-1149
Markdown (Informal)
[Persona Expansion with Commonsense Knowledge for Diverse and Consistent Response Generation](https://aclanthology.org/2023.eacl-main.81) (Kim et al., EACL 2023)
ACL
- Donghyun Kim, Youbin Ahn, Wongyu Kim, Chanhee Lee, Kyungchan Lee, Kyong-Ho Lee, Jeonguk Kim, Donghoon Shin, and Yeonsoo Lee. 2023. Persona Expansion with Commonsense Knowledge for Diverse and Consistent Response Generation. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, pages 1139–1149, Dubrovnik, Croatia. Association for Computational Linguistics.