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        <title>Effect Graph: Effect Relation Extraction for Explanation Generation</title>
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    <name type="personal">
        <namePart type="given">Jonathan</namePart>
        <namePart type="family">Kobbe</namePart>
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    <name type="personal">
        <namePart type="given">Ioana</namePart>
        <namePart type="family">Hulpu\textcommabelows</namePart>
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    <name type="personal">
        <namePart type="given">Heiner</namePart>
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            <title>Proceedings of the 1st Workshop on Natural Language Reasoning and Structured Explanations (NLRSE)</title>
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        <name type="personal">
            <namePart type="given">Bhavana</namePart>
            <namePart type="family">Dalvi Mishra</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Greg</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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            <namePart type="given">Peter</namePart>
            <namePart type="family">Jansen</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Danilo</namePart>
            <namePart type="family">Neves Ribeiro</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Jason</namePart>
            <namePart type="family">Wei</namePart>
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            <publisher>Association for Computational Linguistics</publisher>
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    <abstract>Argumentation is an important means of communication. For describing especially arguments about consequences, the notion of effect relations has been introduced recently. We propose a method to extract effect relations from large text resources and apply it on encyclopedic and argumentative texts. By connecting the extracted relations, we generate a knowledge graph which we call effect graph. For evaluating the effect graph, we perform crowd and expert annotations and create a novel dataset. We demonstrate a possible use case of the effect graph by proposing a method for explaining arguments from consequences.</abstract>
    <identifier type="citekey">kobbe-etal-2023-effect</identifier>
    <identifier type="doi">10.18653/v1/2023.nlrse-1.9</identifier>
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        <date>2023-06</date>
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%0 Conference Proceedings
%T Effect Graph: Effect Relation Extraction for Explanation Generation
%A Kobbe, Jonathan
%A Hulpu\textcommabelows, Ioana
%A Stuckenschmidt, Heiner
%Y Dalvi Mishra, Bhavana
%Y Durrett, Greg
%Y Jansen, Peter
%Y Neves Ribeiro, Danilo
%Y Wei, Jason
%S Proceedings of the 1st Workshop on Natural Language Reasoning and Structured Explanations (NLRSE)
%D 2023
%8 June
%I Association for Computational Linguistics
%C Toronto, Canada
%F kobbe-etal-2023-effect
%X Argumentation is an important means of communication. For describing especially arguments about consequences, the notion of effect relations has been introduced recently. We propose a method to extract effect relations from large text resources and apply it on encyclopedic and argumentative texts. By connecting the extracted relations, we generate a knowledge graph which we call effect graph. For evaluating the effect graph, we perform crowd and expert annotations and create a novel dataset. We demonstrate a possible use case of the effect graph by proposing a method for explaining arguments from consequences.
%R 10.18653/v1/2023.nlrse-1.9
%U https://aclanthology.org/2023.nlrse-1.9
%U https://doi.org/10.18653/v1/2023.nlrse-1.9
%P 116-127

Markdown (Informal)

[Effect Graph: Effect Relation Extraction for Explanation Generation](https://aclanthology.org/2023.nlrse-1.9) (Kobbe et al., NLRSE 2023)

• Effect Graph: Effect Relation Extraction for Explanation Generation (Kobbe et al., NLRSE 2023)

ACL

• Jonathan Kobbe, Ioana Hulpuș, and Heiner Stuckenschmidt. 2023. Effect Graph: Effect Relation Extraction for Explanation Generation. In Proceedings of the 1st Workshop on Natural Language Reasoning and Structured Explanations (NLRSE), pages 116–127, Toronto, Canada. Association for Computational Linguistics.