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        <title>Syn-QG: Syntactic and Shallow Semantic Rules for Question Generation</title>
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            <title>Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics</title>
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            <namePart type="given">Dan</namePart>
            <namePart type="family">Jurafsky</namePart>
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    <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.</abstract>
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    <identifier type="doi">10.18653/v1/2020.acl-main.69</identifier>
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%0 Conference Proceedings
%T Syn-QG: Syntactic and Shallow Semantic Rules for Question Generation
%A Dhole, Kaustubh
%A Manning, Christopher D.
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F dhole-manning-2020-syn
%X 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.
%R 10.18653/v1/2020.acl-main.69
%U https://aclanthology.org/2020.acl-main.69/
%U https://doi.org/10.18653/v1/2020.acl-main.69
%P 752-765

Markdown (Informal)

[Syn-QG: Syntactic and Shallow Semantic Rules for Question Generation](https://aclanthology.org/2020.acl-main.69/) (Dhole & Manning, ACL 2020)

• Syn-QG: Syntactic and Shallow Semantic Rules for Question Generation (Dhole & Manning, ACL 2020)

ACL

• Kaustubh Dhole and Christopher D. Manning. 2020. Syn-QG: Syntactic and Shallow Semantic Rules for Question Generation. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 752–765, Online. Association for Computational Linguistics.