# Natural Question Generation with Reinforcement Learning Based Graph-to-Sequence Model

**Authors:** Yu Chen, Lingfei Wu, Mohammed J. Zaki  
**arXiv:** [1910.08832](https://arxiv.org/abs/1910.08832)  
**Date:** 2019-10-19  
**Summary page:** https://www.summarizepaper.com/fr/paper/1910.08832v1/

## Abstract

Natural question generation (QG) aims to generate questions from a passage and an answer. In this paper, we propose a novel reinforcement learning (RL) based graph-to-sequence (Graph2Seq) model for QG. Our model consists of a Graph2Seq generator where a novel Bidirectional Gated Graph Neural Network is proposed to embed the passage, and a hybrid evaluator with a mixed objective combining both cross-entropy and RL losses to ensure the generation of syntactically and semantically valid text. The proposed model outperforms previous state-of-the-art methods by a large margin on the SQuAD dataset.