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        <title>GEM: Generative Enhanced Model for adversarial attacks</title>
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    <name type="personal">
        <namePart type="given">Piotr</namePart>
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        <namePart type="given">Maria</namePart>
        <namePart type="family">Pszona</namePart>
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        <namePart type="given">Maria</namePart>
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            <title>Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER)</title>
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        <name type="personal">
            <namePart type="given">James</namePart>
            <namePart type="family">Thorne</namePart>
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        <name type="personal">
            <namePart type="given">Andreas</namePart>
            <namePart type="family">Vlachos</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Oana</namePart>
            <namePart type="family">Cocarascu</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Christos</namePart>
            <namePart type="family">Christodoulopoulos</namePart>
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        <name type="personal">
            <namePart type="given">Arpit</namePart>
            <namePart type="family">Mittal</namePart>
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    <abstract>We present our Generative Enhanced Model (GEM) that we used to create samples awarded the first prize on the FEVER 2.0 Breakers Task. GEM is the extended language model developed upon GPT-2 architecture. The addition of novel target vocabulary input to the already existing context input enabled controlled text generation. The training procedure resulted in creating a model that inherited the knowledge of pretrained GPT-2, and therefore was ready to generate natural-like English sentences in the task domain with some additional control. As a result, GEM generated malicious claims that mixed facts from various articles, so it became difficult to classify their truthfulness.</abstract>
    <identifier type="citekey">niewinski-etal-2019-gem</identifier>
    <identifier type="doi">10.18653/v1/D19-6604</identifier>
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%0 Conference Proceedings
%T GEM: Generative Enhanced Model for adversarial attacks
%A Niewinski, Piotr
%A Pszona, Maria
%A Janicka, Maria
%Y Thorne, James
%Y Vlachos, Andreas
%Y Cocarascu, Oana
%Y Christodoulopoulos, Christos
%Y Mittal, Arpit
%S Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F niewinski-etal-2019-gem
%X We present our Generative Enhanced Model (GEM) that we used to create samples awarded the first prize on the FEVER 2.0 Breakers Task. GEM is the extended language model developed upon GPT-2 architecture. The addition of novel target vocabulary input to the already existing context input enabled controlled text generation. The training procedure resulted in creating a model that inherited the knowledge of pretrained GPT-2, and therefore was ready to generate natural-like English sentences in the task domain with some additional control. As a result, GEM generated malicious claims that mixed facts from various articles, so it became difficult to classify their truthfulness.
%R 10.18653/v1/D19-6604
%U https://aclanthology.org/D19-6604
%U https://doi.org/10.18653/v1/D19-6604
%P 20-26

Markdown (Informal)

[GEM: Generative Enhanced Model for adversarial attacks](https://aclanthology.org/D19-6604) (Niewinski et al., 2019)

• GEM: Generative Enhanced Model for adversarial attacks (Niewinski et al., 2019)

ACL

• Piotr Niewinski, Maria Pszona, and Maria Janicka. 2019. GEM: Generative Enhanced Model for adversarial attacks. In Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER), pages 20–26, Hong Kong, China. Association for Computational Linguistics.