copenlu/fever_gold_evidence

Tasks

• text-classification

Citation text from the card

@inproceedings{atanasova-etal-2020-generating,
    title = "Generating Label Cohesive and Well-Formed Adversarial Claims",
    author = "Atanasova, Pepa  and
      Wright, Dustin  and
      Augenstein, Isabelle",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.emnlp-main.256",
    doi = "10.18653/v1/2020.emnlp-main.256",
    pages = "3168--3177",
    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 languag

All datasets marked Permissive or share-alike | JSON