  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.
