  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. We employ multitask
learning with the auxiliary task of answer-aware supporting fact prediction to
guide the question generator. In addition, we also proposed a question-aware
reward function in a Reinforcement Learning (RL) framework to maximize the
utilization of the supporting facts. We demonstrate the effectiveness of our
approach through experiments on the multi-hop question answering dataset,
HotPotQA. Empirical evaluation shows our model to outperform the single-hop
neural question generation models on both automatic evaluation metrics such as
BLEU, METEOR, and ROUGE, and human evaluation metrics for quality and coverage
of the generated questions.
