Papers by Tuna Alikaşifoğlu
VISPool: Enhancing Transformer Encoders with Vector Visibility Graph Neural Networks (2024.findings-acl)
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| Challenge: | Existing graph-based graph construction methods rely on static graphs and are not scalable with increasing document and word counts. |
| Approach: | They propose a dynamic graph construction method based on vector visibility graphs (VVGs) they propose scalable model architecture that integrates VVG convolutional networks into transformer pipelines. |
| Outcome: | The proposed model outperforms baseline models on the GLUE benchmark datasets. |