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    <titleInfo>
        <title>Question Generation Using Sequence-to-Sequence Model with Semantic Role Labels</title>
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    <name type="personal">
        <namePart type="given">Alireza</namePart>
        <namePart type="family">Naeiji</namePart>
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    <name type="personal">
        <namePart type="given">Aijun</namePart>
        <namePart type="family">An</namePart>
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    <name type="personal">
        <namePart type="given">Heidar</namePart>
        <namePart type="family">Davoudi</namePart>
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    <name type="personal">
        <namePart type="given">Marjan</namePart>
        <namePart type="family">Delpisheh</namePart>
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    <name type="personal">
        <namePart type="given">Muath</namePart>
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            <title>Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics</title>
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            <namePart type="given">Andreas</namePart>
            <namePart type="family">Vlachos</namePart>
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        <name type="personal">
            <namePart type="given">Isabelle</namePart>
            <namePart type="family">Augenstein</namePart>
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            <publisher>Association for Computational Linguistics</publisher>
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    <abstract>Automatic generation of questions from text has gained increasing attention due to its useful applications. We propose a novel question generation method that combines the benefits of rule-based and neural sequence-to-sequence (Seq2Seq) models. The proposed method can automatically generate multiple questions from an input sentence covering different views of the sentence as in rule-based methods, while more complicated “rules” can be learned via the Seq2Seq model. The method utilizes semantic role labeling to convert training examples into their semantic representations, and then trains a Seq2Seq model over the semantic representations. Our extensive experiments on three real-world data sets show that the proposed method significantly improves the state-of-the-art neural question generation approaches.</abstract>
    <identifier type="citekey">naeiji-etal-2023-question</identifier>
    <identifier type="doi">10.18653/v1/2023.eacl-main.207</identifier>
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        <date>2023-05</date>
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            <start>2830</start>
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%0 Conference Proceedings
%T Question Generation Using Sequence-to-Sequence Model with Semantic Role Labels
%A Naeiji, Alireza
%A An, Aijun
%A Davoudi, Heidar
%A Delpisheh, Marjan
%A Alzghool, Muath
%Y Vlachos, Andreas
%Y Augenstein, Isabelle
%S Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F naeiji-etal-2023-question
%X Automatic generation of questions from text has gained increasing attention due to its useful applications. We propose a novel question generation method that combines the benefits of rule-based and neural sequence-to-sequence (Seq2Seq) models. The proposed method can automatically generate multiple questions from an input sentence covering different views of the sentence as in rule-based methods, while more complicated “rules” can be learned via the Seq2Seq model. The method utilizes semantic role labeling to convert training examples into their semantic representations, and then trains a Seq2Seq model over the semantic representations. Our extensive experiments on three real-world data sets show that the proposed method significantly improves the state-of-the-art neural question generation approaches.
%R 10.18653/v1/2023.eacl-main.207
%U https://aclanthology.org/2023.eacl-main.207
%U https://doi.org/10.18653/v1/2023.eacl-main.207
%P 2830-2842

Markdown (Informal)

[Question Generation Using Sequence-to-Sequence Model with Semantic Role Labels](https://aclanthology.org/2023.eacl-main.207) (Naeiji et al., EACL 2023)

• Question Generation Using Sequence-to-Sequence Model with Semantic Role Labels (Naeiji et al., EACL 2023)

ACL

• Alireza Naeiji, Aijun An, Heidar Davoudi, Marjan Delpisheh, and Muath Alzghool. 2023. Question Generation Using Sequence-to-Sequence Model with Semantic Role Labels. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, pages 2830–2842, Dubrovnik, Croatia. Association for Computational Linguistics.