  We propose a new character-based text classification framework for
non-alphabetic languages, such as Chinese and Japanese. Our framework consists
of a variational character encoder (VCE) and character-level text classifier.
The VCE is composed of a $\beta$-variational auto-encoder ($\beta$-VAE) that
learns the proposed glyph-aware disentangled character embedding (GDCE). Since
our GDCE provides zero-mean unit-variance character embeddings that are
dimensionally independent, it is applicable for our interpretable data
augmentation, namely, semantic sub-character augmentation (SSA). In this paper,
we evaluated our framework using Japanese text classification tasks at the
document- and sentence-level. We confirmed that our GDCE and SSA not only
provided embedding interpretability but also improved the classification
performance. Our proposal achieved a competitive result to the state-of-the-art
model while also providing model interpretability. Our code is available on
https://github.com/IyatomiLab/GDCE-SSA
