Knowledge Aware Conversation Generation with Explainable Reasoning over Augmented Graphs

Zhibin Liu, Zheng-Yu Niu, Hua Wu, Haifeng Wang

Abstract
Two types of knowledge, triples from knowledge graphs and texts from documents, have been studied for knowledge aware open domain conversation generation, in which graph paths can narrow down vertex candidates for knowledge selection decision, and texts can provide rich information for response generation. Fusion of a knowledge graph and texts might yield mutually reinforcing advantages, but there is less study on that. To address this challenge, we propose a knowledge aware chatting machine with three components, an augmented knowledge graph with both triples and texts, knowledge selector, and knowledge aware response generator. For knowledge selection on the graph, we formulate it as a problem of multi-hop graph reasoning to effectively capture conversation flow, which is more explainable and flexible in comparison with previous works. To fully leverage long text information that differentiates our graph from others, we improve a state of the art reasoning algorithm with machine reading comprehension technology. We demonstrate the effectiveness of our system on two datasets in comparison with state-of-the-art models.
Anthology ID:
D19-1187
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:
1782–1792
Language:
URL:
https://aclanthology.org/D19-1187
DOI:
10.18653/v1/D19-1187
Bibkey:
Cite (ACL):
Zhibin Liu, Zheng-Yu Niu, Hua Wu, and Haifeng Wang. 2019. Knowledge Aware Conversation Generation with Explainable Reasoning over Augmented 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 1782–1792, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
Knowledge Aware Conversation Generation with Explainable Reasoning over Augmented Graphs (Liu et al., EMNLP-IJCNLP 2019)
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PDF:
https://preview.aclanthology.org/ml4al-ingestion/D19-1187.pdf
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