  In this work, we intrinsically and extrinsically evaluate and compare
existing word embedding models for the Armenian language. Alongside, new
embeddings are presented, trained using GloVe, fastText, CBOW, SkipGram
algorithms. We adapt and use the word analogy task in intrinsic evaluation of
embeddings. For extrinsic evaluation, two tasks are employed: morphological
tagging and text classification. Tagging is performed on a deep neural network,
using ArmTDP v2.3 dataset. For text classification, we propose a corpus of news
articles categorized into 7 classes. The datasets are made public to serve as
benchmarks for future models.
