Knowledge-Grounded Dialogue Generation with a Unified Knowledge Representation

Yu Li, Baolin Peng, Yelong Shen, Yi Mao, Lars Liden, Zhou Yu, Jianfeng Gao


Abstract
Knowledge-grounded dialogue systems are challenging to build due to the lack of training data and heterogeneous knowledge sources. Existing systems perform poorly on unseen topics due to limited topics covered in the training data. In addition, it is challenging to generalize to the domains that require different types of knowledge sources. To address the above challenges, we present PLUG, a language model that homogenizes different knowledge sources to a unified knowledge representation for knowledge-grounded dialogue generation tasks. We first retrieve relevant information from heterogeneous knowledge sources (e.g., wiki, dictionary, or knowledge graph); Then the retrieved knowledge is transformed into text and concatenated with dialogue history to feed into the language model for generating responses. PLUG is pre-trained on a large-scale knowledge-grounded dialogue corpus. The empirical evaluation on two benchmarks shows that PLUG generalizes well across different knowledge-grounded dialogue tasks. It achieves comparable performance with state-of-the-art methods in the fully-supervised setting and significantly outperforms other approaches in zero-shot and few-shot settings.
Anthology ID:
2022.naacl-main.15
Volume:
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
July
Year:
2022
Address:
Seattle, United States
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
206–218
Language:
URL:
https://aclanthology.org/2022.naacl-main.15
DOI:
10.18653/v1/2022.naacl-main.15
Bibkey:
Cite (ACL):
Yu Li, Baolin Peng, Yelong Shen, Yi Mao, Lars Liden, Zhou Yu, and Jianfeng Gao. 2022. Knowledge-Grounded Dialogue Generation with a Unified Knowledge Representation. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 206–218, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
Knowledge-Grounded Dialogue Generation with a Unified Knowledge Representation (Li et al., NAACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.naacl-main.15.pdf
Data
InspiredOpenDialKGWizard of Wikipedia