Papers by Semih Yagcioglu
Detecting Cybersecurity Events from Noisy Short Text (N19-1)
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Semih Yagcioglu, Mehmet Saygin Seyfioglu, Begum Citamak, Batuhan Bardak, Seren Guldamlasioglu, Azmi Yuksel, Emin Islam Tatli
| Challenge: | Using domain-specific word embeddings, we propose a method to detect cyber security events from noisy short text. |
| Approach: | They propose a method that leverages domain-specific word embeddings and task-specific features to detect cyber security events from tweets. |
| Outcome: | The proposed model outperforms both baselines and traditional models on a dataset of 2K tweets and manually annotates them. |
Sequential Compositional Generalization in Multimodal Models (2024.naacl-long)
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| Challenge: | a growing number of multimodal models have a limited capacity for generalization . however, prior studies into compositionality have focused on visual grounding and downstream tasks like image captioning. |
| Approach: | They examine compositional generalization using egocentric kitchen activity videos . they find bi-modal and tri-modal models exhibit a clear edge over their text-only counterparts . |
| Outcome: | The proposed model outperforms text-only models in a multimodal setting. |
RecipeQA: A Challenge Dataset for Multimodal Comprehension of Cooking Recipes (D18-1)
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| Challenge: | Existing comprehension tests for QA are limited by the text sources and questionanswer formats. |
| Approach: | They propose a dataset for multimodal comprehension of cooking recipes . preliminary results indicate RecipeQA will serve as a challenging test bed . |
| Outcome: | The proposed dataset will serve as a test bed and ideal benchmark for evaluating machine comprehension systems. |
Harnessing Dataset Cartography for Improved Compositional Generalization in Transformers (2023.findings-emnlp)
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| Challenge: | Existing approaches to understanding compositional generalization of models have focused on novel architectures and alternative learning paradigms. |
| Approach: | They propose a method that harnesses the power of dataset cartography to improve model accuracy by strategically identifying a subset of compositional generalization data. |
| Outcome: | The proposed method improves model accuracy by 10% on CFQ and COGS datasets. |