  Recently hyperbolic geometry has proven to be effective in building
embeddings that encode hierarchical and entailment information. This makes it
particularly suited to modelling the complex asymmetrical relationships between
Chinese characters and words. In this paper we first train a large scale
hyperboloid skip-gram model on a Chinese corpus, then apply the character
embeddings to a downstream hyperbolic Transformer model derived from the
principles of gyrovector space for Poincare disk model. In our experiments the
character-based Transformer outperformed its word-based Euclidean equivalent.
To the best of our knowledge, this is the first time in Chinese NLP that a
character-based model outperformed its word-based counterpart, allowing the
circumvention of the challenging and domain-dependent task of Chinese Word
Segmentation (CWS).
