In this work we implement a training of a Language Model (LM), using Recurrent Neural Network (RNN) and GloVe word embeddings, introduced by Pennigton et al. in [1]. The implementation is following the general idea of training RNNs for LM tasks presented in [2], but is rather using Gated Recurrent Unit (GRU) [3] for a memory cell, and not the more commonly used LSTM [4].
@article{arxiv.1610.03759,
title = {Language Models with Pre-Trained (GloVe) Word Embeddings},
author = {Victor Makarenkov and Bracha Shapira and Lior Rokach},
journal= {arXiv preprint arXiv:1610.03759},
year = {2017}
}