Search Results for author:
Found 10 papers, 6 papers with code
Adversarial Authorship Attribution for Deobfuscation
Our results motivate the need to develop authorship obfuscation approaches that are resistant to deobfuscation.
Don’t sweat the small stuff, classify the rest: Sample Shielding to protect text classifiers against adversarial attacks
Deep learning (DL) is being used extensively for text classification.
VertAttack: Taking advantage of Text Classifiers' horizontal vision
In contrast, humans are easily able to recognize and read words written both horizontally and vertically.
Don't sweat the small stuff, classify the rest: Sample Shielding to protect text classifiers against adversarial attacks
Deep learning (DL) is being used extensively for text classification.
A Girl Has A Name, And It's ... Adversarial Authorship Attribution for Deobfuscation
Specifically, they are not evaluated against adversarially trained authorship attributors that are aware of potential obfuscation.
Suum Cuique: Studying Bias in Taboo Detection with a Community Perspective
In essence, these classifiers represent community level language norms.
On The Robustness of Offensive Language Classifiers
Social media platforms are deploying machine learning based offensive language classification systems to combat hateful, racist, and other forms of offensive speech at scale.
NLP\_UIOWA at Semeval-2021 Task 5: Transferring Toxic Sets to Tag Toxic Spans
We leverage a BLSTM with attention to identify toxic spans in texts.
NLP\_UIOWA at SemEval-2020 Task 8: You're Not the Only One Cursed with Knowledge - Multi Branch Model Memotion Analysis
We propose hybrid models (HybridE and HybridW) for meme analysis (SemEval 2020 Task 8), which involves sentiment classification (Subtask A), humor classification (Subtask B), and scale of semantic classes (Subtask C).
NLP@UIOWA at SemEval-2019 Task 6: Classifying the Crass using Multi-windowed CNNs
This paper proposes a system for OffensEval (SemEval 2019 Task 6), which calls for a system to classify offensive language into several categories.
