| Challenge: | Existing hyperbolic neural networks encode features in the hyperbolical space yet formalize most of their operations in the tangent space. |
| Approach: | They propose a fully hyperbolic framework to build hyperbolical networks based on the Lorentz model by adapting Lorentzer transformations to formalize essential operations of neural networks. |
| Outcome: | The proposed framework has better performance on four NLP tasks compared with existing hyperbolic models . |
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Knowledge Association with Hyperbolic Knowledge Graph Embeddings (2020.emnlp-main)
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A Fully Hyperbolic Neural Model for Hierarchical Multi-Class Classification (2020.findings-emnlp)
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HyperExpan: Taxonomy Expansion with Hyperbolic Representation Learning (2021.findings-emnlp)
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Extracting Event Temporal Relations via Hyperbolic Geometry (2021.emnlp-main)
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