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        <title>A Semantic Role-based Approach to Open-Domain Automatic Question Generation</title>
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            <title>Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications</title>
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            <namePart type="given">Joel</namePart>
            <namePart type="family">Tetreault</namePart>
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            <namePart type="given">Jill</namePart>
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        <name type="personal">
            <namePart type="given">Ekaterina</namePart>
            <namePart type="family">Kochmar</namePart>
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            <namePart type="given">Helen</namePart>
            <namePart type="family">Yannakoudakis</namePart>
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    <abstract>We present a novel rule-based system for automatic generation of factual questions from sentences, using semantic role labeling (SRL) as the main form of text analysis. The system is capable of generating both wh-questions and yes/no questions from the same semantic analysis. We present an extensive evaluation of the system and compare it to a recent neural network architecture for question generation. The SRL-based system outperforms the neural system in both average quality and variety of generated questions.</abstract>
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%0 Conference Proceedings
%T A Semantic Role-based Approach to Open-Domain Automatic Question Generation
%A Flor, Michael
%A Riordan, Brian
%Y Tetreault, Joel
%Y Burstein, Jill
%Y Kochmar, Ekaterina
%Y Leacock, Claudia
%Y Yannakoudakis, Helen
%S Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F flor-riordan-2018-semantic
%X We present a novel rule-based system for automatic generation of factual questions from sentences, using semantic role labeling (SRL) as the main form of text analysis. The system is capable of generating both wh-questions and yes/no questions from the same semantic analysis. We present an extensive evaluation of the system and compare it to a recent neural network architecture for question generation. The SRL-based system outperforms the neural system in both average quality and variety of generated questions.
%R 10.18653/v1/W18-0530
%U https://aclanthology.org/W18-0530
%U https://doi.org/10.18653/v1/W18-0530
%P 254-263

Markdown (Informal)

[A Semantic Role-based Approach to Open-Domain Automatic Question Generation](https://aclanthology.org/W18-0530) (Flor & Riordan, BEA 2018)

• A Semantic Role-based Approach to Open-Domain Automatic Question Generation (Flor & Riordan, BEA 2018)

ACL

• Michael Flor and Brian Riordan. 2018. A Semantic Role-based Approach to Open-Domain Automatic Question Generation. In Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications, pages 254–263, New Orleans, Louisiana. Association for Computational Linguistics.