Conversational Question Answering with Reformulations over Knowledge Graph
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866914732764561408 |
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| author | Liu, Lihui Hill, Blaine Du, Boxin Wang, Fei Tong, Hanghang |
| author_facet | Liu, Lihui Hill, Blaine Du, Boxin Wang, Fei Tong, Hanghang |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2312_17269 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Conversational Question Answering with Reformulations over Knowledge Graph Liu, Lihui Hill, Blaine Du, Boxin Wang, Fei Tong, Hanghang Computation and Language Artificial Intelligence 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. |
| title | Conversational Question Answering with Reformulations over Knowledge Graph |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2312.17269 |