  Unsupervised word representation learning from large corpora is badly needed
for downstream tasks such as text classification, information retrieval, and
machine translation. The representation precision of the fastText language
models is mostly due to their use of subword information. In previous work, the
optimization of fastText subword sizes has been largely neglected, and
non-English fastText language models were trained using subword sizes optimized
for English and German.
  In our work, we train English, German, Czech, and Italian fastText language
models on Wikipedia, and we optimize the subword sizes on the English, German,
Czech, and Italian word analogy tasks. We show that the optimization of subword
sizes results in a 5% improvement on the Czech word analogy task. We also show
that computationally expensive hyperparameter optimization can be replaced with
cheap $n$-gram frequency analysis: subword sizes that are the closest to
covering 3.76% of all unique subwords in a language are shown to be the optimal
fastText hyperparameters on the English, German, Czech, and Italian word
analogy tasks.
