Toward Subgraph-Guided Knowledge Graph Question Generation with Graph Neural Networks

TL;DR

This paper introduces a graph neural network-based approach for generating natural language questions from KG subgraphs, improving over previous methods by explicitly modeling graph structure and enabling node attribute copying, leading to state-of-the-art results.

Contribution

The paper proposes a novel bidirectional Graph2Seq model with node-level copying for KG question generation from subgraphs, capturing explicit structure and enhancing question quality.

Findings

Achieves new state-of-the-art scores on two QG benchmarks.

Outperforms existing methods significantly in automatic and human evaluations.

Benefits the Question Answering task through data augmentation.

Abstract

Knowledge graph (KG) question generation (QG) aims to generate natural language questions from KGs and target answers. Previous works mostly focus on a simple setting which is to generate questions from a single KG triple. In this work, we focus on a more realistic setting where we aim to generate questions from a KG subgraph and target answers. In addition, most of previous works built on either RNN-based or Transformer based models to encode a linearized KG sugraph, which totally discards the explicit structure information of a KG subgraph. To address this issue, we propose to apply a bidirectional Graph2Seq model to encode the KG subgraph. Furthermore, we enhance our RNN decoder with node-level copying mechanism to allow directly copying node attributes from the KG subgraph to the output question. Both automatic and human evaluation results demonstrate that our model achieves new state-of-the-art scores, outperforming existing methods by a significant margin on two QG benchmarks. Experimental results also show that our QG model can consistently benefit the Question Answering (QA) task as a mean of data augmentation.