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.

我们提出了一个基于强化学习模型的 CornNet，该模型利用大型语言模型生成的问题重述来改进对知识图谱中的多轮自然语言问题的回答，实验证明 CornNet 优于现有的 ConvQA 模型。