---
abstract: 'We study the task of generating from Wikipedia articles question-answer pairs that cover content beyond a single sentence. We propose a neural network approach that incorporates coreference knowledge via a novel gating mechanism. Compared to models that only take into account sentence-level information [@heilman2010good; @du2017LearningToAsk; @zhou2017neural], we find that the linguistic knowledge introduced by the coreference representation aids question generation significantly, producing models that outperform the current state-of-the-art. We apply our system (composed of an answer span extraction system and the passage-level QG system) to the 10,000 top-ranking Wikipedia articles and create a corpus of over one million question-answer pairs. We also provide a qualitative analysis for this large-scale generated corpus from Wikipedia.'
author:
- |
    Xinya Du  [and]{}  Claire Cardie\
    Department of Computer Science\
    Cornell University\
    Ithaca, NY, 14853, USA\
    [{xdu, cardie}@cs.cornell.edu]{}\
bibliography:
- 'acl2018.bib'
title: |
    Harvesting Paragraph-Level Question-Answer Pairs\
    from Wikipedia
---

Introduction
============

Recently, there has been a resurgence of work in NLP on reading comprehension [@hermann2015teaching; @rajpurkar2016squad; @JoshiTriviaQA2017] with the goal of developing systems that can answer questions about the content of a given passage or document. Large-scale QA datasets are indispensable for training expressive statistical models for this task and play a 