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There is a huge imbalance between languages currently spoken andcorresponding resources to study them. Most of the attention naturally goes tothe "big" languages: those which have the largest presence in terms of mediaand number of speakers. Other less represented languages sometimes do not evenhave a good quality corpus to study them. In this paper, we tackle thisimbalance by presenting a new set of evaluation resources for Tatar, a languageof the Turkic language family which is mainly spoken in Tatarstan Republic,Russia.  We present three datasets: Similarity and Relatedness datasets that consistof human scored word pairs and can be used to evaluate semantic models; andAnalogies dataset that comprises analogy questions and allows to exploresemantic, syntactic, and morphological aspects of language modeling. All threedatasets build upon existing datasets for the English language and follow thesame structure. However, they are not mere translations. They take into accountspecifics of the Tatar language and expand beyond the original datasets. Weevaluate state-of-the-art word embedding models for two languages using ourproposed datasets for Tatar and the original datasets for English and reportour findings on performance comparison.

SART - Similarity, Analogies, and Relatedness for Tatar Language: New Benchmark Datasets for Word Embeddings Evaluation