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- W4385570892 abstract "We present our submission to SemEval-2023 Task 10: Explainable Detection of Online Sexism (EDOS). We address all three tasks: Task A consists of identifying whether a post is sexist. If so, Task B attempts to assign it one of four categories: threats, derogation, animosity, and prejudiced discussions. Task C aims for an even more fine-grained classification, divided among 11 classes. Our team UniBoe’s experiments with fine-tuning of hate-tuned Transformer-based models and priming for generative models. In addition, we explore model-agnostic strategies, such as data augmentation techniques combined with active learning, as well as obfuscation of identity terms. Our official submissions obtain an F1_score of 0.83 for Task A, 0.58 for Task B and 0.32 for Task C." @default.
- W4385570892 created "2023-08-05" @default.
- W4385570892 creator A5050386238 @default.
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- W4385570892 date "2023-01-01" @default.
- W4385570892 modified "2023-09-24" @default.
- W4385570892 title "UniBoe’s at SemEval-2023 Task 10: Model-Agnostic Strategies for the Improvement of Hate-Tuned and Generative Models in the Classification of Sexist Posts" @default.
- W4385570892 doi "https://doi.org/10.18653/v1/2023.semeval-1.158" @default.
- W4385570892 hasPublicationYear "2023" @default.
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