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HyperText: Endowing FastText with Hyperbolic Geometry

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arxiv 2010.16143 v3 pith:33AQ5AAI submitted 2020-10-30 cs.CL cs.LG

Yudong Zhu , Di Zhou , Jinghui Xiao , Xin Jiang , Xiao Chen , Qun Liu This is my paper · ORCID
classification cs.CLcs.LG
keywords fasttexthyperbolichypertexttextclassificationdataendowinggeometry
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Natural language data exhibit tree-like hierarchical structures such as the hypernym-hyponym relations in WordNet. FastText, as the state-of-the-art text classifier based on shallow neural network in Euclidean space, may not model such hierarchies precisely with limited representation capacity. Considering that hyperbolic space is naturally suitable for modeling tree-like hierarchical data, we propose a new model named HyperText for efficient text classification by endowing FastText with hyperbolic geometry. Empirically, we show that HyperText outperforms FastText on a range of text classification tasks with much reduced parameters.

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  1. Adversarial Attacks on Hyperbolic Networks

    cs.LG 2024-12 conditional novelty 5.0 of 10

    Hyperbolic versions of FGM and PGD attacks are proposed, and experiments show that Euclidean and hyperbolic ResNets have different, geometry-specific adversarial vulnerabilities.

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