TopKG: Target-oriented Dialog via Global Planning on Knowledge Graph
Zhitong Yang | Bo Wang | Jinfeng Zhou | Yue Tan | Dongming Zhao | Kun Huang | Ruifang He | Yuexian Hou
Proceedings of the 29th International Conference on Computational Linguistics
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.
- Zhitong Yang 1
- Bo Wang 1
- Jinfeng Zhou 1
- Dongming Zhao 1
- Kun Huang 1
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