Neural Question Generation

Question generation attempts to generate a natural language question given a passage and an answer. Most state-of-the-art methods have focused on generating simple questions involving single-hop relations and based on a single or a few sentences. In this project, we focus on generating multi-hop questions which requires discovering and modeling the multiple entities and their semantic relations in the passage. To that end, we use the HotpotQA dataset, a multi-document and multi-hop dataset for questions answering that provides not only the context, question, and answer but also the supporting facts that lead to the answer. To solve the problem, we propose the use of transformer-based models, which have shown to perform well in single-hop question generation, and we study different variants to condition the model using the context and the supporting facts