@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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Guiding Dialogue Agents to Complex Semantic Targets by Dynamically Completing Knowledge Graph
%A Tan, Yue
%A Wang, Bo
%A Liu, Anqi
%A Zhao, Dongming
%A Huang, Kun
%A He, Ruifang
%A Hou, Yuexian
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F tan-etal-2023-guiding
%X 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.
%R 10.18653/v1/2023.findings-acl.407
%U https://aclanthology.org/2023.findings-acl.407
%U https://doi.org/10.18653/v1/2023.findings-acl.407
%P 6506-6518
Markdown (Informal)
[Guiding Dialogue Agents to Complex Semantic Targets by Dynamically Completing Knowledge Graph](https://aclanthology.org/2023.findings-acl.407) (Tan et al., Findings 2023)
ACL