利用内在对偶性实现多跳问题生成
Exploiting Intrinsic Duality for Multi-Hop Question Generation
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中文总结 AI 辅助
该研究针对现有多跳问题生成（MQG）忽略其与问答（QA）内在对偶性的局限，提出QQ框架，通过双向对齐约束与对比学习利用对偶性，在HotpotQA和MuSiQue数据集上显著提升多跳问题生成质量。
中文摘要 AI 辅助
多跳问题生成（MQG）旨在从多篇给定文档和目标答案中生成问题，而问答（QA）则专注于在给定特定问题的情况下从文档中推导答案。尽管MQG和QA本质上是对偶任务，但大多数现有的MQG研究在很大程度上忽略了这种内在对偶性。为解决这一局限，我们提出了QQ，一个利用问答之间的对偶性实现多跳问题生成的新框架。具体而言，QQ采用了一个统一架构，同时充当MQG和QA模型，以充分利用二者的相互依赖关系。我们的框架由两个关键机制驱动：（i）实施双向对齐约束，以确保MQG模型生成的问题与QA模型生成的答案之间存在严格的对应关系；（ii）应用对比学习，将配对的问答表示拉近，同时将未配对的表示推远，从而强化这种对应关系。在HotpotQA和MuSiQue数据集上进行的大量自动和人工评估表明，QQ框架显著提升了生成的多跳问题的质量。
英文摘要
Multi hop question generation (MQG) aims to generate questions from multiple given documents and target answers, whereas question answering (QA) focuses on deriving answers from documents given specific questions. Although MQG and QA are inherently dual tasks, most existing MQG studies largely overlook this intrinsic duality. To address this limitation, we propose QQ, a novel framework that exploits the duality between Question and answer for multi hop Question generation. Specifically, QQ employs a unified architecture functioning simultaneously as both an MQG and a QA model to fully leverage their interdependence. Our framework is driven by two key mechanisms: (i) enforcing bidirectional alignment constraints to ensure strict mutual correspondence between the questions generated by the MQG model and the answers produced by the QA model; and (ii) applying contrastive learning to pull paired question answer representations closer while pushing unpaired ones apart, thereby reinforcing this correspondence. Extensive automatic and human evaluations on the HotpotQA and MuSiQue datasets demonstrate that the QQ framework significantly improves the quality of generated multi hop questions.
