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Found 7 papers, 1 papers with code
Do Transformers know symbolic rules, and would we know if they did?
To facilitate this, we experiment on four sequence modelling tasks on the T5 Transformer in two experiment settings: zero-shot generalization, and generalization across class-specific vocabularies flipped between the training and test set.
Good Artists Copy, Great Artists Steal: Model Extraction Attacks Against Image Translation Models
We present a framework for conducting such attacks, and show that an adversary can successfully extract functional surrogate models by querying $F_V$ using data from the same domain as the training data for $F_V$.
A little goes a long way: Improving toxic language classification despite data scarcity
Detection of some types of toxic language is hampered by extreme scarcity of labeled training data.
Effective writing style imitation via combinatorial paraphrasing
Finally, we highlight a critical problem that afflicts all current style transfer techniques: the adversary can use the same technique for thwarting style transfer via adversarial training.
EAT: a simple and versatile semantic representation format for multi-purpose NLP
We present the first generic semantic representation format for NLP directly based on these insights.
Text Analysis in Adversarial Settings: Does Deception Leave a Stylistic Trace?
Textual deception constitutes a major problem for online security.
All You Need is "Love": Evading Hate-speech Detection
With the spread of social networks and their unfortunate use for hate speech, automatic detection of the latter has become a pressing problem.
