@inproceedings{yang-etal-2022-topkg,
title = "{T}op{KG}: Target-oriented Dialog via Global Planning on Knowledge Graph",
author = "Yang, Zhitong and
Wang, Bo and
Zhou, Jinfeng and
Tan, Yue and
Zhao, Dongming and
Huang, Kun and
He, Ruifang and
Hou, Yuexian",
booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2022.coling-1.62",
pages = "745--755",
abstract = "Target-oriented dialog aims to reach a global target through multi-turn conversation. The key to the task is the global planning towards the target, which flexibly guides the dialog concerning the context. However, existing target-oriented dialog works take a local and greedy strategy for response generation, where global planning is absent. In this work, we propose global planning for target-oriented dialog on a commonsense knowledge graph (KG). We design a global reinforcement learning with the planned paths to flexibly adjust the local response generation model towards the global target. We also propose a KG-based method to collect target-oriented samples automatically from the chit-chat corpus for model training. Experiments show that our method can reach the target with a higher success rate, fewer turns, and more coherent responses.",
}
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<abstract>Target-oriented dialog aims to reach a global target through multi-turn conversation. The key to the task is the global planning towards the target, which flexibly guides the dialog concerning the context. However, existing target-oriented dialog works take a local and greedy strategy for response generation, where global planning is absent. In this work, we propose global planning for target-oriented dialog on a commonsense knowledge graph (KG). We design a global reinforcement learning with the planned paths to flexibly adjust the local response generation model towards the global target. We also propose a KG-based method to collect target-oriented samples automatically from the chit-chat corpus for model training. Experiments show that our method can reach the target with a higher success rate, fewer turns, and more coherent responses.</abstract>
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%0 Conference Proceedings
%T TopKG: Target-oriented Dialog via Global Planning on Knowledge Graph
%A Yang, Zhitong
%A Wang, Bo
%A Zhou, Jinfeng
%A Tan, Yue
%A Zhao, Dongming
%A Huang, Kun
%A He, Ruifang
%A Hou, Yuexian
%S Proceedings of the 29th International Conference on Computational Linguistics
%D 2022
%8 October
%I International Committee on Computational Linguistics
%C Gyeongju, Republic of Korea
%F yang-etal-2022-topkg
%X Target-oriented dialog aims to reach a global target through multi-turn conversation. The key to the task is the global planning towards the target, which flexibly guides the dialog concerning the context. However, existing target-oriented dialog works take a local and greedy strategy for response generation, where global planning is absent. In this work, we propose global planning for target-oriented dialog on a commonsense knowledge graph (KG). We design a global reinforcement learning with the planned paths to flexibly adjust the local response generation model towards the global target. We also propose a KG-based method to collect target-oriented samples automatically from the chit-chat corpus for model training. Experiments show that our method can reach the target with a higher success rate, fewer turns, and more coherent responses.
%U https://aclanthology.org/2022.coling-1.62
%P 745-755
Markdown (Informal)
[TopKG: Target-oriented Dialog via Global Planning on Knowledge Graph](https://aclanthology.org/2022.coling-1.62) (Yang et al., COLING 2022)
ACL
- Zhitong Yang, Bo Wang, Jinfeng Zhou, Yue Tan, Dongming Zhao, Kun Huang, Ruifang He, and Yuexian Hou. 2022. TopKG: Target-oriented Dialog via Global Planning on Knowledge Graph. In Proceedings of the 29th International Conference on Computational Linguistics, pages 745–755, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.