@inproceedings{huang-etal-2021-plato,
title = "{PLATO}-{KAG}: Unsupervised Knowledge-Grounded Conversation via Joint Modeling",
author = "Huang, Xinxian and
He, Huang and
Bao, Siqi and
Wang, Fan and
Wu, Hua and
Wang, Haifeng",
editor = "Papangelis, Alexandros and
Budzianowski, Pawe{\l} and
Liu, Bing and
Nouri, Elnaz and
Rastogi, Abhinav and
Chen, Yun-Nung",
booktitle = "Proceedings of the 3rd Workshop on Natural Language Processing for Conversational AI",
month = nov,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.nlp4convai-1.14",
doi = "10.18653/v1/2021.nlp4convai-1.14",
pages = "143--154",
abstract = "Large-scale conversation models are turning to leveraging external knowledge to improve the factual accuracy in response generation. Considering the infeasibility to annotate the external knowledge for large-scale dialogue corpora, it is desirable to learn the knowledge selection and response generation in an unsupervised manner. In this paper, we propose PLATO-KAG (Knowledge-Augmented Generation), an unsupervised learning approach for end-to-end knowledge-grounded conversation modeling. For each dialogue context, the top-k relevant knowledge elements are selected and then employed in knowledge-grounded response generation. The two components of knowledge selection and response generation are optimized jointly and effectively under a balanced objective. Experimental results on two publicly available datasets validate the superiority of PLATO-KAG.",
}
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<abstract>Large-scale conversation models are turning to leveraging external knowledge to improve the factual accuracy in response generation. Considering the infeasibility to annotate the external knowledge for large-scale dialogue corpora, it is desirable to learn the knowledge selection and response generation in an unsupervised manner. In this paper, we propose PLATO-KAG (Knowledge-Augmented Generation), an unsupervised learning approach for end-to-end knowledge-grounded conversation modeling. For each dialogue context, the top-k relevant knowledge elements are selected and then employed in knowledge-grounded response generation. The two components of knowledge selection and response generation are optimized jointly and effectively under a balanced objective. Experimental results on two publicly available datasets validate the superiority of PLATO-KAG.</abstract>
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%0 Conference Proceedings
%T PLATO-KAG: Unsupervised Knowledge-Grounded Conversation via Joint Modeling
%A Huang, Xinxian
%A He, Huang
%A Bao, Siqi
%A Wang, Fan
%A Wu, Hua
%A Wang, Haifeng
%Y Papangelis, Alexandros
%Y Budzianowski, Paweł
%Y Liu, Bing
%Y Nouri, Elnaz
%Y Rastogi, Abhinav
%Y Chen, Yun-Nung
%S Proceedings of the 3rd Workshop on Natural Language Processing for Conversational AI
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online
%F huang-etal-2021-plato
%X Large-scale conversation models are turning to leveraging external knowledge to improve the factual accuracy in response generation. Considering the infeasibility to annotate the external knowledge for large-scale dialogue corpora, it is desirable to learn the knowledge selection and response generation in an unsupervised manner. In this paper, we propose PLATO-KAG (Knowledge-Augmented Generation), an unsupervised learning approach for end-to-end knowledge-grounded conversation modeling. For each dialogue context, the top-k relevant knowledge elements are selected and then employed in knowledge-grounded response generation. The two components of knowledge selection and response generation are optimized jointly and effectively under a balanced objective. Experimental results on two publicly available datasets validate the superiority of PLATO-KAG.
%R 10.18653/v1/2021.nlp4convai-1.14
%U https://aclanthology.org/2021.nlp4convai-1.14
%U https://doi.org/10.18653/v1/2021.nlp4convai-1.14
%P 143-154
Markdown (Informal)
[PLATO-KAG: Unsupervised Knowledge-Grounded Conversation via Joint Modeling](https://aclanthology.org/2021.nlp4convai-1.14) (Huang et al., NLP4ConvAI 2021)
ACL