Yudong Zhu


2020

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HyperText: Endowing FastText with Hyperbolic Geometry
Yudong Zhu | Di Zhou | Jinghui Xiao | Xin Jiang | Xiao Chen | Qun Liu
Findings of the Association for Computational Linguistics: EMNLP 2020

Natural language data exhibit tree-like hierarchical structures such as the hypernym-hyponym hierarchy in WordNet. FastText, as the state-of-the-art text classifier based on shallow neural network in Euclidean space, may not represent such hierarchies precisely with limited representation capacity. Considering that hyperbolic space is naturally suitable for modelling 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.