  Unsupervised representation learning of words from large multilingual corpora
is useful for downstream tasks such as word sense disambiguation, semantic text
similarity, and information retrieval. The representation precision of
log-bilinear fastText models is mostly due to their use of subword information.
In previous work, the optimization of fastText's subword sizes has not been
fully explored, and non-English fastText models were trained using subword
sizes optimized for English and German word analogy tasks. In our work, we find
the optimal subword sizes on the English, German, Czech, Italian, Spanish,
French, Hindi, Turkish, and Russian word analogy tasks. We then propose a
simple n-gram coverage model and we show that it predicts better-than-default
subword sizes on the Spanish, French, Hindi, Turkish, and Russian word analogy
tasks. We show that the optimization of fastText's subword sizes matters and
results in a 14% improvement on the Czech word analogy task. We also show that
expensive parameter optimization can be replaced by a simple n-gram coverage
model that consistently improves the accuracy of fastText models on the word
analogy tasks by up to 3% compared to the default subword sizes, and that it is
within 1% accuracy of the optimal subword sizes.
