Machine Learning, NLP, Deep Learning, Sentiment Analysis, Word Embeddings.
that pre-trained word embeddings lead to a faster training and a lower final training loss. It can be interpreted that the model could pick up more semantic signals from the pre-trained embeddings than it did from the training data through the embedding layer. We see from the results of the TREC