Reinforced Multi-task Approach for Multi-hop Question Generation

Understand

Question generation (QG) attempts to solve the inverse of question answering (QA) problem by generating a natural language question given a document and an answer.

• While sequence to sequence neural models surpass rule-based systems for QG, they are limited in their capacity to focus on more than one supporting fact.
• For QG, we often require multiple supporting facts to generate high-quality questions.
• Inspired by recent works on multi-hop reasoning in QA, we take up Multi-hop question generation, which aims at generating relevant questions based on supporting facts in the context.