  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.
