@inproceedings{sun-etal-2022-stylized,
title = "Stylized Knowledge-Grounded Dialogue Generation via Disentangled Template Rewriting",
author = "Sun, Qingfeng and
Xu, Can and
Hu, Huang and
Wang, Yujing and
Miao, Jian and
Geng, Xiubo and
Chen, Yining and
Xu, Fei and
Jiang, Daxin",
editor = "Carpuat, Marine and
de Marneffe, Marie-Catherine and
Meza Ruiz, Ivan Vladimir",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.241",
doi = "10.18653/v1/2022.naacl-main.241",
pages = "3304--3318",
abstract = "Current Knowledge-Grounded Dialogue Generation (KDG) models specialize in producing rational and factual responses. However, to establish long-term relationships with users, the KDG model needs the capability to generate responses in a desired style or attribute. Thus, we study a new problem: Stylized Knowledge-Grounded Dialogue Generation (SKDG). It presents two challenges: (1) How to train a SKDG model where no {\textless}context, knowledge, stylized response{\textgreater} triples are available. (2) How to cohere with context and preserve the knowledge when generating a stylized response. In this paper, we propose a novel disentangled template rewriting (DTR) method which generates responses via combing disentangled style templates (from monolingual stylized corpus) and content templates (from KDG corpus). The entire framework is end-to-end differentiable and learned without supervision. Extensive experiments on two benchmarks indicate that DTR achieves a significant improvement on all evaluation metrics compared with previous state-of-the-art stylized dialogue generation methods. Besides, DTR achieves comparable performance with the state-of-the-art KDG methods in standard KDG evaluation setting.",
}
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<abstract>Current Knowledge-Grounded Dialogue Generation (KDG) models specialize in producing rational and factual responses. However, to establish long-term relationships with users, the KDG model needs the capability to generate responses in a desired style or attribute. Thus, we study a new problem: Stylized Knowledge-Grounded Dialogue Generation (SKDG). It presents two challenges: (1) How to train a SKDG model where no \textlesscontext, knowledge, stylized response\textgreater triples are available. (2) How to cohere with context and preserve the knowledge when generating a stylized response. In this paper, we propose a novel disentangled template rewriting (DTR) method which generates responses via combing disentangled style templates (from monolingual stylized corpus) and content templates (from KDG corpus). The entire framework is end-to-end differentiable and learned without supervision. Extensive experiments on two benchmarks indicate that DTR achieves a significant improvement on all evaluation metrics compared with previous state-of-the-art stylized dialogue generation methods. Besides, DTR achieves comparable performance with the state-of-the-art KDG methods in standard KDG evaluation setting.</abstract>
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%0 Conference Proceedings
%T Stylized Knowledge-Grounded Dialogue Generation via Disentangled Template Rewriting
%A Sun, Qingfeng
%A Xu, Can
%A Hu, Huang
%A Wang, Yujing
%A Miao, Jian
%A Geng, Xiubo
%A Chen, Yining
%A Xu, Fei
%A Jiang, Daxin
%Y Carpuat, Marine
%Y de Marneffe, Marie-Catherine
%Y Meza Ruiz, Ivan Vladimir
%S Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F sun-etal-2022-stylized
%X Current Knowledge-Grounded Dialogue Generation (KDG) models specialize in producing rational and factual responses. However, to establish long-term relationships with users, the KDG model needs the capability to generate responses in a desired style or attribute. Thus, we study a new problem: Stylized Knowledge-Grounded Dialogue Generation (SKDG). It presents two challenges: (1) How to train a SKDG model where no \textlesscontext, knowledge, stylized response\textgreater triples are available. (2) How to cohere with context and preserve the knowledge when generating a stylized response. In this paper, we propose a novel disentangled template rewriting (DTR) method which generates responses via combing disentangled style templates (from monolingual stylized corpus) and content templates (from KDG corpus). The entire framework is end-to-end differentiable and learned without supervision. Extensive experiments on two benchmarks indicate that DTR achieves a significant improvement on all evaluation metrics compared with previous state-of-the-art stylized dialogue generation methods. Besides, DTR achieves comparable performance with the state-of-the-art KDG methods in standard KDG evaluation setting.
%R 10.18653/v1/2022.naacl-main.241
%U https://aclanthology.org/2022.naacl-main.241
%U https://doi.org/10.18653/v1/2022.naacl-main.241
%P 3304-3318
Markdown (Informal)
[Stylized Knowledge-Grounded Dialogue Generation via Disentangled Template Rewriting](https://aclanthology.org/2022.naacl-main.241) (Sun et al., NAACL 2022)
ACL
- Qingfeng Sun, Can Xu, Huang Hu, Yujing Wang, Jian Miao, Xiubo Geng, Yining Chen, Fei Xu, and Daxin Jiang. 2022. Stylized Knowledge-Grounded Dialogue Generation via Disentangled Template Rewriting. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 3304–3318, Seattle, United States. Association for Computational Linguistics.