Papers with universal trigger generation

1 papers
Generating Label Cohesive and Well-Formed Adversarial Claims (2020.emnlp-main)

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Challenge: Existing work on adversarial triggers for fact checking models reveals weaknesses and flaws of models . universal adversarials often inadvertently invert the meaning of instances they are inserted in .
Approach: They propose a method for automatically generating highly potent, well-formed, label cohesive claims for FC using universal adversarial triggers.
Outcome: The proposed method maintains the directionality and semantic validity of the claim better than previous work on the FEVER dataset.

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