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        <title>Generating Label Cohesive and Well-Formed Adversarial Claims</title>
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        <namePart type="given">Pepa</namePart>
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    <name type="personal">
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    <name type="personal">
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        <name type="personal">
            <namePart type="given">Bonnie</namePart>
            <namePart type="family">Webber</namePart>
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        <name type="personal">
            <namePart type="given">Trevor</namePart>
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        <name type="personal">
            <namePart type="given">Yang</namePart>
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    <abstract>Adversarial attacks reveal important vulnerabilities and flaws of trained models. One potent type of attack are universal adversarial triggers, which are individual n-grams that, when appended to instances of a class under attack, can trick a model into predicting a target class. However, for inference tasks such as fact checking, these triggers often inadvertently invert the meaning of instances they are inserted in. In addition, such attacks produce semantically nonsensical inputs, as they simply concatenate triggers to existing samples. Here, we investigate how to generate adversarial attacks against fact checking systems that preserve the ground truth meaning and are semantically valid. We extend the HotFlip attack algorithm used for universal trigger generation by jointly minimizing the target class loss of a fact checking model and the entailment class loss of an auxiliary natural language inference model. We then train a conditional language model to generate semantically valid statements, which include the found universal triggers. We find that the generated attacks maintain the directionality and semantic validity of the claim better than previous work.</abstract>
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    <identifier type="doi">10.18653/v1/2020.emnlp-main.256</identifier>
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        <date>2020-11</date>
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%0 Conference Proceedings
%T Generating Label Cohesive and Well-Formed Adversarial Claims
%A Atanasova, Pepa
%A Wright, Dustin
%A Augenstein, Isabelle
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F atanasova-etal-2020-generating
%X Adversarial attacks reveal important vulnerabilities and flaws of trained models. One potent type of attack are universal adversarial triggers, which are individual n-grams that, when appended to instances of a class under attack, can trick a model into predicting a target class. However, for inference tasks such as fact checking, these triggers often inadvertently invert the meaning of instances they are inserted in. In addition, such attacks produce semantically nonsensical inputs, as they simply concatenate triggers to existing samples. Here, we investigate how to generate adversarial attacks against fact checking systems that preserve the ground truth meaning and are semantically valid. We extend the HotFlip attack algorithm used for universal trigger generation by jointly minimizing the target class loss of a fact checking model and the entailment class loss of an auxiliary natural language inference model. We then train a conditional language model to generate semantically valid statements, which include the found universal triggers. We find that the generated attacks maintain the directionality and semantic validity of the claim better than previous work.
%R 10.18653/v1/2020.emnlp-main.256
%U https://aclanthology.org/2020.emnlp-main.256
%U https://doi.org/10.18653/v1/2020.emnlp-main.256
%P 3168-3177

Markdown (Informal)

[Generating Label Cohesive and Well-Formed Adversarial Claims](https://aclanthology.org/2020.emnlp-main.256) (Atanasova et al., EMNLP 2020)

• Generating Label Cohesive and Well-Formed Adversarial Claims (Atanasova et al., EMNLP 2020)

ACL

• Pepa Atanasova, Dustin Wright, and Isabelle Augenstein. 2020. Generating Label Cohesive and Well-Formed Adversarial Claims. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 3168–3177, Online. Association for Computational Linguistics.