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Reinforcement learning from constraints and focal entity shifting in conversational KGQA

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Abstract

The actual needs of users for information are often hidden in multiple question answering (QA) on the same topic. In order to generate answers to users’ current questions, a conversational QA system relies on getting external information from a knowledge graph (KG) and combining it with conversation history. Therefore, how to make full use of the information of KG and combining it with the conversation environment is the top priority. In a conversational knowledge graph question answering (KGQA) scenario, the follow-up questions are often incomplete and may contain a shift of focal entity. Considering and processing the constrained information which plays the key role in solving complex conversational KGQA is very important. In this paper, we propose a reinforcement learning (RL) model, which uses a dynamically maintained context entity set to capture the shift of the focal entity in the process of conversation. We then use the bidirectional encoder representations from transformers (BERT) pre-training model to obtain the semantic information from context questions and KG paths. Our model learns from not only the 1-hop path but also 2-hop path constraint of the KG at the same time and gives reward rules based on precision and certain rules, respectively. Compared with state-of-the-art methods on ConvQuestions, our model improves mean reciprocal rank (MRR) and precision at 1 (P@1) by a margin of 4.7\(\%\) and 4.3\(\%\), respectively. Experimental results on two datasets demonstrate the effectiveness of our proposed approach.

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Data availability

All data analyzed during this study are from the online demo website of CONVEX [5] (https://convex.mpi-inf.mpg.de) and CONQUER [15] (https://conquer.mpi-inf.mpg.de/).

Notes

  1. https://github.com/PhilippChr/wikidata-core-for-QA.

  2. https://convex.mpi-inf.mpg.de.

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Acknowledgements

The research work was supported by the Natural Science Foundation of China under Grant No.U21A20491, No.U1936109, No.U1908214, No.KLSA201906.

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Authors and Affiliations

  1. School of Computer Science and Technology, Dalian University of Technology, No. 2 Linggong Road, Dalian City, 116024, Liaoning Province, China

    Xirong Xu, Tao Xu, Ziming Wang, Haochen Li & Xiaopeng Wei

  2. School of Control Science and Engineering, Dalian University of Technology, No. 2 Linggong Road, Dalian City, 116024, Liaoning Province, China

    Li Zhu

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Xu, X., Xu, T., Wang, Z. et al. Reinforcement learning from constraints and focal entity shifting in conversational KGQA. Neural Comput & Applic 36, 2015–2028 (2024). https://doi.org/10.1007/s00521-023-09138-z

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