Papers with TextFooler
A Semantic-based Method for Unsupervised Commonsense Question Answering (2021.acl-long)
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| Challenge: | Existing methods to score candidates without labeled task data are difficult to use . e.g., pre-trained language models can be easily affected by irrelevant factors . |
| Approach: | They propose a method that generates plausible answers with generative models and uses them to select the correct answer. |
| Outcome: | The proposed method achieves the best results in unsupervised situations. |
ReinforceBug: A Framework to Generate Adversarial Textual Examples (2021.naacl-main)
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| Challenge: | Recent studies have demonstrated that ML Models are sensitive to Adversarial Examples (AEs) AEs are generated by perturbingining examples that preserve the intrinsic utility of the ML solutions but influence the classifier's predictions between original and modified inputs. |
| Approach: | They propose a reinforcement learning framework that learns a policy that is transferable on unseen datasets and generates utility-preserving and transferable AEs. |
| Outcome: | The proposed framework is 10% more successful than the state-of-the-art attack TextFooler. |
Contrasting Human- and Machine-Generated Word-Level Adversarial Examples for Text Classification (2021.emnlp-main)
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| Challenge: | Recent work has raised the question of whether valid adversarial inputs are feasible. |
| Approach: | They analyze how human-generated adversarial examples compare to the best algorithms . they use crowdsourcing to modify words in an input text with immediate feedback . |
| Outcome: | The proposed algorithms are not more efficient than the best to generate natural-reading, sentiment-preserving examples. |