  This work examines the possibility of using syllable embeddings, instead of
the often used $n$-gram embeddings, as subword embeddings. We investigate this
for two languages: English and Dutch. To this end, we also translated two
standard English word embedding evaluation datasets, WordSim353 and
SemEval-2017, to Dutch. Furthermore, we provide the research community with
data sets of syllabic decompositions for both languages. We compare our
approach to full word and $n$-gram embeddings. Compared to full word
embeddings, we obtain English models that are 20 to 30 times smaller while
retaining 80% of the performance. For Dutch, models are 15 times smaller for
70% performance retention. Although less accurate than the $n$-gram baseline we
used, our models can be trained in a matter of minutes, as opposed to hours for
the $n$-gram approach. We identify a path toward upgrading performance in
future work. All code is made publicly available, as well as our collected
English and Dutch syllabic decompositions and Dutch evaluation set
translations.
