Russian language model is trained using the fast.ai version of AWD LSTM Language Model (basically LSTM with dropouts) with data from news site Lenta.ru. Achieved perplexity is 36.23 (90/10 validation split), compared to state-of-the-art on November 17, 2017 at 40.68 for English language. To the best of my knowledge, there is no comparable research in the Russian language at the point of writing (May 8, 2018). Details can be found in the notebook RLM.ipynb. The model can be found on Google Disk.
There are several word embeddings trained on Russian corpora: fastText, word2vec, RusVectōrēs. Why would you choose to work with language model instead? The reason is simple: it is a 3-layer neural net with non-linear activations and dropouts. While embeddings are just 300-dimensional vectors in a linear space. More complex word representation allows to capture more sophisticated language peculiarities. It also makes transfer learning very easy - adding a simple custom head will work perfectly for classification problems as well as regression tasks. Check out a similar model trained on OpenSubtitles dataset which got me the second place at Yandex Algorithm 2018 ML-track.
- Train model on wikipedia dump
- Beat SentiRuEval-2016 benchmarks