Papers by Emanuel Sallinger
SpeedE: Euclidean Geometric Knowledge Graph Embedding Strikes Back (2024.findings-naacl)
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| Challenge: | Geometric knowledge graph embedding models (gKGEs) have shown great potential for knowledge graph completion (KGC) however, contemporary gKges require high embeddable dimensionalities or complex embeddances for good KGC performance, drastically limiting their time and space efficiency. |
| Approach: | They propose a lightweight Euclidean gKGE that provides strong inference capabilities and significantly outperforms state-of-the-art gGKGEs. |
| Outcome: | The proposed model outperforms state-of-the-art gKGEs on YAGO3-10 and WN18RR while significantly increasing their efficiency. |
Linear-Time and Constant-Memory Text Embeddings Based on Recurrent Language Models (2026.acl-long)
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| Challenge: | Existing work on recurrent models for text embedding is limited to small task-specific models. |
| Approach: | They propose a vertically chunked inference strategy that enables fast embedding generation with memory usage that becomes constant in the input length once it exceeds the vertical chunk size. |
| Outcome: | The proposed architectures achieve competitive performance across benchmarks while maintaining a substantially smaller memory footprint compared to transformer-based models. |