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• W3034630799 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." @default.
• W3034630799 created "2020-06-19" @default.
• W3034630799 creator A5046006076 @default.
• W3034630799 creator A5061120974 @default.
• W3034630799 date "2020-01-01" @default.
• W3034630799 modified "2023-10-01" @default.
• W3034630799 title "Syn-QG: Syntactic and Shallow Semantic Rules for Question Generation" @default.
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• W3034630799 doi "https://doi.org/10.18653/v1/2020.acl-main.69" @default.
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