@inproceedings{tan-etal-2023-guiding,
    title = "Guiding Dialogue Agents to Complex Semantic Targets by Dynamically Completing Knowledge Graph",
    author = "Tan, Yue  and
      Wang, Bo  and
      Liu, Anqi  and
      Zhao, Dongming  and
      Huang, Kun  and
      He, Ruifang  and
      Hou, Yuexian",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.findings-acl.407",
    doi = "10.18653/v1/2023.findings-acl.407",
    pages = "6506--6518",
    abstract = "In the target-oriented dialogue, the representation and achievement of targets are two interrelated essential issues. In current approaches, the target is typically supposed to be a single object represented as a word, which makes it relatively easy to achieve the target through dialogue with the help of a knowledge graph (KG). However, when the target has complex semantics, the existing knowledge graph is often incomplete in tracking complex semantic relations. This paper studies target-oriented dialog where the target is a topic sentence. We combine the methods of knowledge retrieval and relationship prediction to construct a context-related dynamic KG. On dynamic KG, we can track the implicit semantic paths in the speaker{'}s mind that may not exist in the existing KGs. In addition, we also designed a novel metric to evaluate the tracked path automatically. The experimental results show that our method can control the agent more logically and smoothly toward the complex target.",
}

In the target-oriented dialogue, the representation and achievement of targets are two interrelated essential issues. In current approaches, the target is typically supposed to be a single object represented as a word, which makes it relatively easy to achieve the target through dialogue with the help of a knowledge graph (KG). However, when the target has complex semantics, the existing knowledge graph is often incomplete in tracking complex semantic relations. This paper studies target-oriented dialog where the target is a topic sentence. We combine the methods of knowledge retrieval and relationship prediction to construct a context-related dynamic KG. On dynamic KG, we can track the implicit semantic paths in the speaker{'}s mind that may not exist in the existing KGs. In addition, we also designed a novel metric to evaluate the tracked path automatically. The experimental results show that our method can control the agent more logically and smoothly toward the complex target.
Tan, Yue Wang, Bo Liu, Anqi Zhao, Dongming Huang, Kun He, Ruifang Hou, Yuexian
Guiding Dialogue Agents to Complex Semantic Targets by Dynamically Completing Knowledge Graph