---
abstract: 'This paper makes one of the first efforts toward automatically generating *complex* questions from knowledge graphs. Particularly, we study how to leverage existing simple question datasets for this task, under two separate scenarios: using either sub-questions of the target complex questions, or distantly related pseudo sub-questions when the former are unavailable. First, a competitive base model named is designed to map mplex query raphs to natural language uestions. Afterwards, we propose two extension models, namely and , respectively for the above two scenarios. The former encodes and copies from a sub-question, while the latter further scores and aggregates multiple pseudo sub-questions. Experiment results show that the extension models significantly outperform not only base , but also its augmented variant using simple questions as additional training examples. This demonstrates the importance of *instance-level* connections between simple and corresponding complex questions, which may be underexploited by straightforward data augmentation of that builds *model-level* connections through learned parameters.'
author:
- |
    Jie Zhao, Xiang Deng, Huan Sun,\
    The Ohio State University\
    {zhao.1359, deng.595, sun.397}@osu.edu
bibliography:
- 'bibliography.bib'
title: 'Easy-to-Hard: Leveraging Simple Questions for Complex Question Generation'
---


