Natural Question Generation with Reinforcement Learning Based Graph-to-Sequence Model
arXiv:1910.08832
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.
4 pages. Accepted at the NeurIPS 2019 Workshop on Graph Representation Learning (NeurIPS GRL 2019). Final Version. arXiv admin note: substantial text overlap with arXiv:1908.04942
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