DyKgChat: Benchmarking Dialogue Generation Grounding on Dynamic Knowledge Graphs

Yi-Lin Tuan, Yun-Nung Chen, Hung-yi Lee

Abstract
Data-driven, knowledge-grounded neural conversation models are capable of generating more informative responses. However, these models have not yet demonstrated that they can zero-shot adapt to updated, unseen knowledge graphs. This paper proposes a new task about how to apply dynamic knowledge graphs in neural conversation model and presents a novel TV series conversation corpus (DyKgChat) for the task. Our new task and corpus aids in understanding the influence of dynamic knowledge graphs on responses generation. Also, we propose a preliminary model that selects an output from two networks at each time step: a sequence-to-sequence model (Seq2Seq) and a multi-hop reasoning model, in order to support dynamic knowledge graphs. To benchmark this new task and evaluate the capability of adaptation, we introduce several evaluation metrics and the experiments show that our proposed approach outperforms previous knowledge-grounded conversation models. The proposed corpus and model can motivate the future research directions.
Anthology ID:
D19-1194
Volume:
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Month:
November
Year:
2019
Address:
Hong Kong, China
Editors:
Kentaro Inui, Jing Jiang, Vincent Ng, Xiaojun Wan
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
1855–1865
Language:
URL:
https://aclanthology.org/D19-1194
DOI:
10.18653/v1/D19-1194
Bibkey:
Cite (ACL):
Yi-Lin Tuan, Yun-Nung Chen, and Hung-yi Lee. 2019. DyKgChat: Benchmarking Dialogue Generation Grounding on Dynamic Knowledge Graphs. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 1855–1865, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
DyKgChat: Benchmarking Dialogue Generation Grounding on Dynamic Knowledge Graphs (Tuan et al., EMNLP-IJCNLP 2019)
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PDF:
https://preview.aclanthology.org/nschneid-patch-4/D19-1194.pdf
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 D19-1194.Attachment.pdf
Code
 Pascalson/DyKGChat
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