---
abstract: 'Question Generation (QG) is fundamentally a simple syntactic transformation; however, many aspects of semantics influence what questions are good to form. We implement this observation by developing Syn-QG, a set of transparent syntactic rules leveraging universal dependencies, shallow semantic parsing, lexical resources, and custom rules which transform declarative sentences into question-answer pairs. We utilize PropBank argument descriptions and VerbNet state predicates to incorporate shallow semantic content, which helps generate questions of a descriptive nature and produce inferential and semantically richer questions than existing systems. In order to improve syntactic fluency and eliminate grammatically incorrect questions, we employ back-translation over the output of these syntactic rules. A set of crowd-sourced evaluations shows that our system can generate a larger number of highly grammatical and relevant questions than previous QG systems and that back-translation drastically improves grammaticality at a slight cost of generating irrelevant questions.'
author:
- |
    Kaustubh D. Dhole\
    Amelia Science\
    RnD, IPsoft\
    New York, NY 10004\
    `kdhole@ipsoft.com`\
    Christopher D. Manning\
    Department of Computer Science\
    Stanford University\
    Stanford, CA 94305\
    `manning@stanford.edu`\
bibliography:
- 'anthology.bib'
- 'acl2020.bib'
title: 'Syn-QG: Syntactic and Shallow Semantic Rules for Question Generation'
---

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

Automatic Question Generation (QG) is the task of generating 