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Rewriting Meaningful Sentences via Conditional BERT Sampling and an application on fooling text classifiers
- Publication Year :
- 2020
-
Abstract
- Most adversarial attack methods that are designed to deceive a text classifier change the text classifier's prediction by modifying a few words or characters. Few try to attack classifiers by rewriting a whole sentence, due to the difficulties inherent in sentence-level rephrasing as well as the problem of setting the criteria for legitimate rewriting. In this paper, we explore the problem of creating adversarial examples with sentence-level rewriting. We design a new sampling method, named ParaphraseSampler, to efficiently rewrite the original sentence in multiple ways. Then we propose a new criteria for modification, called a sentence-level threaten model. This criteria allows for both word- and sentence-level changes, and can be adjusted independently in two dimensions: semantic similarity and grammatical quality. Experimental results show that many of these rewritten sentences are misclassified by the classifier. On all 6 datasets, our ParaphraseSampler achieves a better attack success rate than our baseline.<br />Comment: Please see an updated version of this paper at arXiv:2104.08453
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.2010.11869
- Document Type :
- Working Paper