This paper addresses the problem of generating questions from a given context and an answer, specifically focusing on questions that require multi-hop reasoning across an extended context. Previous studies have suggested that key phrase selection is essential for question generation (QG), yet it is still challenging to connect such disjointed phrases into meaningful questions, particularly for long context. To mitigate this issue, we propose MultiFactor, a novel QG framework based on multi-level content planning. Specifically, MultiFactor includes two components: FA-model, which simultaneously selects key phrases and generates full answers, and Q-model which takes the generated full answer as an additional input to generate questions. Here, full answer generation is introduced to connect the short answer with the selected key phrases, thus forming an answer-aware summary to facilitate QG. Both FA-model and Q-model are formalized as simple-yet-effective Phrase-Enhanced Transformers, our joint model for phrase selection and text generation. Experimental results show that our method outperforms strong baselines on two popular QG datasets. Our code is available at https://github.com/zeaver/MultiFactor.

翻译：本文针对基于给定上下文和答案生成问题这一任务展开研究，特别聚焦于需要跨扩展上下文进行多跳推理的问题。已有研究表明，关键短语选择对问题生成至关重要，但如何将这些离散的短语连接成有意义的问句仍具挑战性，尤其当上下文较长时。为缓解该问题，我们提出MultiFactor——一种基于多层次内容规划的新型问题生成框架。具体而言，MultiFactor包含两个组件：FA模型（用于同步选择关键短语并生成完整答案）和Q模型（以生成的完整答案为额外输入进行问题生成）。其中，完整答案生成旨在将短答案与选定的关键短语建立关联，从而形成答案感知的摘要以促进问题生成。FA模型和Q模型均被形式化为简单高效的短语增强型Transformer——我们提出的短语选择与文本生成联合模型。实验结果表明，我们的方法在两个主流问题生成数据集上均优于强基线模型。相关代码已在 https://github.com/zeaver/MultiFactor 开源。