Conversational Question Answering with Reformulations over Knowledge Graph

• URL: http://arxiv.org/abs/2312.17269v2
• Date: Fri, 29 Mar 2024 06:32:18 GMT
• Title: Conversational Question Answering with Reformulations over Knowledge Graph
• Authors: Lihui Liu, Blaine Hill, Boxin Du, Fei Wang, Hanghang Tong,
• Abstract summary: State-of-the-art methods of ConvQA often struggle with inexplicit question-answer pairs. We propose a reinforcement learning based model, CornNet, to improve ConvQA performance. CornNet learns question representations using human writing reformulations, and a student model to mimic the teacher model's output.
• Score: 48.581924841203325
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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