Papers with deep learning-based approaches
Rule By Example: Harnessing Logical Rules for Explainable Hate Speech Detection (2023.acl-long)
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| Challenge: | Existing approaches to content moderation are based on rule-based heuristics, but they lack the flexibility and robustness needed to moderate harmful content. |
| Approach: | They propose a novel contrastive learning approach for learning from logical rules for content moderation using only a few data examples. |
| Outcome: | The proposed approach outperforms state-of-the-art deep learning classifiers while providing more explainable predictions. |
Transferring Knowledge via Neighborhood-Aware Optimal Transport for Low-Resource Hate Speech Detection (2022.aacl-main)
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| Challenge: | Existing approaches to detect hate speech are expensive and time-consuming . a new approach allows for flexible learning of neighborhood information . |
| Approach: | They propose a method that allows flexible modeling of neighbors retrieved from a resource-rich corpus to learn the amount of transfer. |
| Outcome: | The proposed training strategy improves on low-resource hate speech corpora over baselines. |
Visual-Textual Entailment with Quantities Using Model Checking and Knowledge Injection (2024.lrec-main)
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| Challenge: | Visual-textual entailment (VTE) is a critical task in multimodal inference. |
| Approach: | They propose a visual-textual entailment system that solves VTE tasks with quantities and negation. |
| Outcome: | The proposed system solves visual-textual entailment tasks with quantities and negation more robustly than previous approaches. |