Papers by Zakir Durumeric
TATA: Stance Detection via Topic-Agnostic and Topic-Aware Embeddings (2023.emnlp-main)
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| Challenge: | Recent studies have focused on topic-specific stance classifiers that fail to generalize to unseen topics. |
| Approach: | They propose to use contrastive learning and an unlabeled dataset to train topic-agnostic/TAG and topic-aligned/TAW embeddings for use in downstream stance detection. |
| Outcome: | The proposed model achieves state-of-the-art performance across several public stance detection datasets (0.771 F1-score on the Zero-shot VAST dataset). |
Hierarchical Level-Wise News Article Clustering via Multilingual Matryoshka Embeddings (2025.acl-long)
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| Challenge: | Contextual large language model embeddings are often monolingual, do not scale, and struggle in multilingual settings. |
| Approach: | They propose a hierarchical approach to embed news articles and social media data using Matryoshka embeddings that can determine story similarity at varying levels of granularity based on which subset of dimensions is examined. |
| Outcome: | The proposed model achieves state-of-the-art performance on the SemEval 2022 task 8 dataset. |