Papers by Dan Vilenchik
HARALD: Augmenting Hate Speech Data Sets with Real Data (2022.findings-emnlp)
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| Challenge: | Hate speech detection depends on the availability of variable labeled data. |
| Approach: | They propose a method that uses real unlabelled data from online platforms to augment existing models by harvesting and processing it. |
| Outcome: | The proposed approach improves the classification performance of hate speech classification models. |
Acquired TASTE: Multimodal Stance Detection with Textual and Structural Embeddings (2025.coling-main)
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| Challenge: | Prior work has demonstrated the importance of the conversational context in stance detection. |
| Approach: | They propose a multimodal architecture for stance detection that fuses transformer-based content embedding with unsupervised structural embeddment. |
| Outcome: | The proposed model outperforms strong baselines on common benchmarks and outperformed existing models on common frameworks. |