Language Models with Pre-Trained (GloVe) Word Embeddings

Victor Makarenkov, Bracha Shapira, Lior Rokach

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].

Knowledge Graph

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