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arXiv · 2312.17269

Conversational Question Answering with Reformulations over Knowledge Graph

Lihui Liu·Blaine Hill·Boxin Du·Fei Wang·Hanghang Tong
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Abstract

Conversational question answering (convQA) over knowledge graphs (KGs) involves answering multi-turn natural language questions about information contained in a KG. State-of-the-art methods of ConvQA often struggle with inexplicit question-answer pairs. These inputs are easy for human beings to understand given a conversation history, but hard for a machine to interpret, which can degrade ConvQA performance. To address this problem, we propose a reinforcement learning (RL) based model, CornNet, which utilizes question reformulations generated by large language models (LLMs) to improve ConvQA performance. CornNet adopts a teacher-student architecture where a teacher model learns question representations using human writing reformulations, and a student model to mimic the teacher model's output via reformulations generated by LLMs. The learned question representation is then used by an RL model to locate the correct answer in a KG. Extensive experimental results show that CornNet outperforms state-of-the-art convQA models.

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Lihui Liu, Blaine Hill, Boxin Du, Fei Wang, Hanghang Tong. 2024-03-29. Conversational Question Answering with Reformulations over Knowledge Graph. https://arxiv.org/abs/2312.17269

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