Syn-QG: Syntactic and Shallow Semantic Rules for Question Generation

• URL: http://arxiv.org/abs/2004.08694v5
• Date: Mon, 28 Nov 2022 18:02:14 GMT
• Title: Syn-QG: Syntactic and Shallow Semantic Rules for Question Generation
• Authors: Kaustubh D. Dhole and Christopher D. Manning
• Abstract summary: We develop SynQG, a set of transparent syntactic rules which transform declarative sentences into question-answer pairs. We utilize PropBank argument descriptions and VerbNet state predicates to incorporate shallow semantic content. In order to improve syntactic fluency and eliminate grammatically incorrect questions, we employ back-translation over the output of these syntactic rules.
• Score: 49.671882751569534
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 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.

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