GenEx: A Commonsense-aware Unified Generative Framework for Explainable Cyberbullying Detection (2023.emnlp-main)
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| Challenge: | a significant gap exists in understanding code-mixed languages and the need for explainability in this context. |
| Approach: | They propose to annotate posts with four labels to identify bullies in code-mixed languages . they propose to use a generative framework to reimagine the multitask problem as a text-to-text generation task. |
| Outcome: | The proposed model outperforms baseline models and state-of-the-art models on the BullyExplain dataset. |
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| Challenge: | Existing methods for detecting cyberbullying rely on text analysis of social media sessions. |
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Peeking inside the black box: A Commonsense-aware Generative Framework for Explainable Complaint Detection (2023.acl-long)
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| Challenge: | Complaining is an expression of negative emotions communicated due to a discrepancy between reality and expectations. |
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Kanishk Verma, Sri Balaaji, Joachim Wagner, Arefeh Kazemi, Darragh Mccashin, Isobel Walsh@dcu, Sayani Basak, Sinan Asci, Yelena Cherkasova, Alexandros Poulis, James Ohiggins Norman, Rebecca Umbach Umbach, Tijana Milosevic, Brian Davis
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| Challenge: | Recent laws like “right to explanations” have spurred research in developing interpretable models . a recent study has shown that multimodal explanations improve performance in generating textual justifications . |
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Generation-Based Data Augmentation for Offensive Language Detection: Is It Worth It? (2023.eacl-main)
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| Challenge: | generative data augmentation has been shown to be effective in offensive language detection but the potential for bias injection has not been investigated. |
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Kanishk Verma, Kolawole John Adebayo, Joachim Wagner, Megan Reynolds, Rebecca Umbach, Tijana Milosevic, Brian Davis
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| Challenge: | Current content moderation systems fail to protect children from harmful content, especially in under-resourced, code-switched settings. |
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Offensive Content Detection via Synthetic Code-Switched Text (2022.coling-1)
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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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MemeIntel: Explainable Detection of Propagandistic and Hateful Memes (2025.emnlp-main)
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| Challenge: | Existing methods for label detection and explanation generation have been limited in understanding complex issues . identifying propaganda and hate in memes is essential for combating misinformation and minimizing harm . |
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