Zhibin Liu


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PLATO-2: Towards Building an Open-Domain Chatbot via Curriculum Learning
Siqi Bao | Huang He | Fan Wang | Hua Wu | Haifeng Wang | Wenquan Wu | Zhen Guo | Zhibin Liu | Xinchao Xu
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021


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Knowledge Aware Conversation Generation with Explainable Reasoning over Augmented Graphs
Zhibin Liu | Zheng-Yu Niu | Hua Wu | Haifeng Wang
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

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.