Papers by Hyeseon Ahn
AmpleHate: Amplifying the Attention for Versatile Implicit Hate Detection (2025.emnlp-main)
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| Challenge: | Current approaches to detect hate speech rely on contrastive learning to distinguish hate from non-hate sentences. |
| Approach: | They propose a novel approach to detect implicit hate speech by identifying explicit targets . they use a pretrained Named Entity Recognition model to capture explicit target information . |
| Outcome: | The proposed approach outperforms current methods and achieves faster convergence. |
TCProF:Time-Complexity Prediction SSL Framework (2025.naacl-long)
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| Challenge: | determining the precise time complexity of a code is theoretically undecidable . determining time complexity is a challenging task in programming efficiency analysis . |
| Approach: | They propose a time-complexity prediction SSL framework that uses code snippets labeled with their time complexity classes to predict code time. |
| Outcome: | The proposed approach improves performance by 60% over self-training methods. |
Analyzing Offensive Language Dataset Insights from Training Dynamics and Human Agreement Level (2025.coling-main)
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| Challenge: | Existing models struggle to generalize themselves when applied to implicit cases, especially in out-of-domain scenarios. |
| Approach: | They propose a data refinement strategy that integrates human annotation agreement with model training dynamics to enhance model performance and generalization. |
| Outcome: | The proposed approach improves performance across multiple datasets and models while retaining ambiguous cases crucial for out-of-domain generalization. |
SharedCon: Implicit Hate Speech Detection using Shared Semantics (2024.findings-acl)
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| Challenge: | Recent studies suggest that classifying hateful posts in a binary manner may not address nuanced task of detecting implicit hate speech. |
| Approach: | They propose a contrastive learning approach that leverages shared semantics among data to detect implicit hate speech. |
| Outcome: | The proposed approach is based on a clustering-based contrastive learning approach with human-written implications or machine-generated augmented data. |