“Define Your Terms” : Enhancing Efficient Offensive Speech Classification with Definition (2024.eacl-long)
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| Challenge: | Multiple studies have proposed various semantically related yet subtle distinct categories of offensive speech. |
| Approach: | They propose a meta-learning architecture that incorporates the input’s label and definition for classification via Prototypical Network. |
| Outcome: | The proposed model achieves 75% of the maximal F1-score while using less than 10% of the available training data across 4 datasets. |
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Leon Derczynski, Marco Guerini, Debora Nozza, Flor Miriam Plaza-del-Arco, Jeffrey Sorensen, Marcos Zampieri
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| Challenge: | Existing methods to detect offensive content in social media platforms are limited by the availability of labeled code-switched data. |
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