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""" |
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PWWS |
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======= |
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(Generating Natural Language Adversarial Examples through Probability Weighted Word Saliency) |
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""" |
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from textattack import Attack |
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from textattack.constraints.pre_transformation import ( |
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RepeatModification, |
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StopwordModification, |
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) |
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from textattack.goal_functions import UntargetedClassification |
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from textattack.search_methods import GreedyWordSwapWIR |
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from textattack.transformations import WordSwapWordNet |
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from .attack_recipe import AttackRecipe |
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class PWWSRen2019(AttackRecipe): |
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"""An implementation of Probability Weighted Word Saliency from "Generating |
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Natural Language Adversarial Examples through Probability Weighted Word |
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Saliency", Ren et al., 2019. |
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Words are prioritized for a synonym-swap transformation based on |
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a combination of their saliency score and maximum word-swap effectiveness. |
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Note that this implementation does not include the Named |
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Entity adversarial swap from the original paper, because it requires |
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access to the full dataset and ground truth labels in advance. |
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https://www.aclweb.org/anthology/P19-1103/ |
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""" |
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@staticmethod |
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def build(model_wrapper): |
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transformation = WordSwapWordNet() |
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constraints = [RepeatModification(), StopwordModification()] |
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goal_function = UntargetedClassification(model_wrapper) |
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search_method = GreedyWordSwapWIR("weighted-saliency") |
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return Attack(goal_function, constraints, transformation, search_method) |
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