HyperText: Endowing FastText with Hyperbolic Geometry

Yudong Zhu, Di Zhou, Jinghui Xiao, Xin Jiang, Xiao Chen, Qun Liu


Abstract
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.
Anthology ID:
2020.findings-emnlp.104
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2020
Month:
November
Year:
2020
Address:
Online
Editors:
Trevor Cohn, Yulan He, Yang Liu
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1166–1171
Language:
URL:
https://aclanthology.org/2020.findings-emnlp.104
DOI:
10.18653/v1/2020.findings-emnlp.104
Bibkey:
Cite (ACL):
Yudong Zhu, Di Zhou, Jinghui Xiao, Xin Jiang, Xiao Chen, and Qun Liu. 2020. HyperText: Endowing FastText with Hyperbolic Geometry. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 1166–1171, Online. Association for Computational Linguistics.
Cite (Informal):
HyperText: Endowing FastText with Hyperbolic Geometry (Zhu et al., Findings 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.findings-emnlp.104.pdf
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