  In text processing, deep neural networks mostly use word embeddings as an
input. Embeddings have to ensure that relations between words are reflected
through distances in a high-dimensional numeric space. To compare the quality
of different text embeddings, typically, we use benchmark datasets. We present
a collection of such datasets for the word analogy task in nine languages:
Croatian, English, Estonian, Finnish, Latvian, Lithuanian, Russian, Slovenian,
and Swedish. We redesigned the original monolingual analogy task to be much
more culturally independent and also constructed cross-lingual analogy datasets
for the involved languages. We present basic statistics of the created datasets
and their initial evaluation using fastText embeddings.
