论文标题
Syn-QG：问题产生的句法和浅层语义规则
Syn-QG: Syntactic and Shallow Semantic Rules for Question Generation
论文作者
论文摘要
问题产生（QG）从根本上是一种简单的句法变换。但是，语义的许多方面都会影响哪些问题形成。我们通过开发Synqg来实现这一观察，这是一套透明的句法规则，利用了普遍的依赖性，浅层语义解析，词汇资源和自定义规则，这些规则将声明性句子转化为问答式的句子对。我们利用Propbank的论点描述和动词状态谓词来纳入浅层语义内容，这有助于产生描述性质的问题，并产生比现有系统更具推论性和语义上富裕的问题。为了提高句法流利度并消除语法错误的问题，我们对这些句法规则的输出进行了反翻译。一系列人群评估表明，与以前的QG系统相比，我们的系统可以产生更多的高度语法和相关问题，并且反向翻译大大提高语法性，以略有成本引起无关的问题。
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 SynQG, 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.
