Learning to Improve Persona Consistency in Multi-party Dialogue Generation via Text Knowledge Enhancement
Dongshi Ju, Shi Feng, Pengcheng Lv, Daling Wang, Yifei Zhang
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Abstract
In an open-domain dialogue system, the consistent persona is a key factor to generate real and coherent dialogues. Existing methods suffer from the incomprehensive persona tags that have unique and obscure meanings to describe human’s personality. Besides, the addressee information, which is closely related to express personality in multi-party dialogues, has been neglected. In this paper, we construct a multi-party personalized dialogue dataset and propose a graph convolution network model (PersonaTKG) with addressee selecting mechanism that integrates personas, dialogue utterances, and external text knowledge in a unified graph. Extensive experiments have shown that PersonaTKG outperforms the baselines by large margins and effectively improves persona consistency in the generated responses.- Anthology ID:
- 2022.coling-1.23
- Volume:
- Proceedings of the 29th International Conference on Computational Linguistics
- Month:
- October
- Year:
- 2022
- Address:
- Gyeongju, Republic of Korea
- Editors:
- Nicoletta Calzolari, Chu-Ren Huang, Hansaem Kim, James Pustejovsky, Leo Wanner, Key-Sun Choi, Pum-Mo Ryu, Hsin-Hsi Chen, Lucia Donatelli, Heng Ji, Sadao Kurohashi, Patrizia Paggio, Nianwen Xue, Seokhwan Kim, Younggyun Hahm, Zhong He, Tony Kyungil Lee, Enrico Santus, Francis Bond, Seung-Hoon Na
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 298–309
- Language:
- URL:
- https://aclanthology.org/2022.coling-1.23/
- DOI:
- Bibkey:
- Cite (ACL):
- Dongshi Ju, Shi Feng, Pengcheng Lv, Daling Wang, and Yifei Zhang. 2022. Learning to Improve Persona Consistency in Multi-party Dialogue Generation via Text Knowledge Enhancement. In Proceedings of the 29th International Conference on Computational Linguistics, pages 298–309, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
- Cite (Informal):
- Learning to Improve Persona Consistency in Multi-party Dialogue Generation via Text Knowledge Enhancement (Ju et al., COLING 2022)
- Copy Citation:
- PDF:
- https://aclanthology.org/2022.coling-1.23.pdf
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- HLA-Chat
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@inproceedings{ju-etal-2022-learning,
title = "Learning to Improve Persona Consistency in Multi-party Dialogue Generation via Text Knowledge Enhancement",
author = "Ju, Dongshi and
Feng, Shi and
Lv, Pengcheng and
Wang, Daling and
Zhang, Yifei",
editor = "Calzolari, Nicoletta and
Huang, Chu-Ren and
Kim, Hansaem and
Pustejovsky, James and
Wanner, Leo and
Choi, Key-Sun and
Ryu, Pum-Mo and
Chen, Hsin-Hsi and
Donatelli, Lucia and
Ji, Heng and
Kurohashi, Sadao and
Paggio, Patrizia and
Xue, Nianwen and
Kim, Seokhwan and
Hahm, Younggyun and
He, Zhong and
Lee, Tony Kyungil and
Santus, Enrico and
Bond, Francis and
Na, Seung-Hoon",
booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2022.coling-1.23/",
pages = "298--309",
abstract = "In an open-domain dialogue system, the consistent persona is a key factor to generate real and coherent dialogues. Existing methods suffer from the incomprehensive persona tags that have unique and obscure meanings to describe human`s personality. Besides, the addressee information, which is closely related to express personality in multi-party dialogues, has been neglected. In this paper, we construct a multi-party personalized dialogue dataset and propose a graph convolution network model (PersonaTKG) with addressee selecting mechanism that integrates personas, dialogue utterances, and external text knowledge in a unified graph. Extensive experiments have shown that PersonaTKG outperforms the baselines by large margins and effectively improves persona consistency in the generated responses."
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%0 Conference Proceedings %T Learning to Improve Persona Consistency in Multi-party Dialogue Generation via Text Knowledge Enhancement %A Ju, Dongshi %A Feng, Shi %A Lv, Pengcheng %A Wang, Daling %A Zhang, Yifei %Y Calzolari, Nicoletta %Y Huang, Chu-Ren %Y Kim, Hansaem %Y Pustejovsky, James %Y Wanner, Leo %Y Choi, Key-Sun %Y Ryu, Pum-Mo %Y Chen, Hsin-Hsi %Y Donatelli, Lucia %Y Ji, Heng %Y Kurohashi, Sadao %Y Paggio, Patrizia %Y Xue, Nianwen %Y Kim, Seokhwan %Y Hahm, Younggyun %Y He, Zhong %Y Lee, Tony Kyungil %Y Santus, Enrico %Y Bond, Francis %Y Na, Seung-Hoon %S Proceedings of the 29th International Conference on Computational Linguistics %D 2022 %8 October %I International Committee on Computational Linguistics %C Gyeongju, Republic of Korea %F ju-etal-2022-learning %X In an open-domain dialogue system, the consistent persona is a key factor to generate real and coherent dialogues. Existing methods suffer from the incomprehensive persona tags that have unique and obscure meanings to describe human‘s personality. Besides, the addressee information, which is closely related to express personality in multi-party dialogues, has been neglected. In this paper, we construct a multi-party personalized dialogue dataset and propose a graph convolution network model (PersonaTKG) with addressee selecting mechanism that integrates personas, dialogue utterances, and external text knowledge in a unified graph. Extensive experiments have shown that PersonaTKG outperforms the baselines by large margins and effectively improves persona consistency in the generated responses. %U https://aclanthology.org/2022.coling-1.23/ %P 298-309
Markdown (Informal)
[Learning to Improve Persona Consistency in Multi-party Dialogue Generation via Text Knowledge Enhancement](https://aclanthology.org/2022.coling-1.23/) (Ju et al., COLING 2022)
- Learning to Improve Persona Consistency in Multi-party Dialogue Generation via Text Knowledge Enhancement (Ju et al., COLING 2022)
ACL
- Dongshi Ju, Shi Feng, Pengcheng Lv, Daling Wang, and Yifei Zhang. 2022. Learning to Improve Persona Consistency in Multi-party Dialogue Generation via Text Knowledge Enhancement. In Proceedings of the 29th International Conference on Computational Linguistics, pages 298–309, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.