# Reinforced Multi-task Approach for Multi-hop Question Generation

arXiv: https://arxiv.org/abs/2004.02143 · HTML: https://mlsanity.com/paper/2004.02143/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Apr 2020.

## Built on by (most cited first, 3 of 3)

- [Reinforcement Learning for Abstractive Question Summarization with Question-aware Semantic Rewards](https://mlsanity.com/paper/2107.00176/) (2021)
- [QA4QG: Using Question Answering to Constrain Multi-Hop Question Generation](https://mlsanity.com/paper/2202.06538/) (2022)
- [Towards Answering Health-related Questions from Medical Videos: Datasets and Approaches](https://mlsanity.com/paper/2309.12224/) (2023)

## Abstract (arXiv)

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.