It's also possible to initialise a word embedding matrix with vectors trained with word2vec (or any other type of pre-trained embeddings: fastText, GLoVe being

It's also possible to initialise a word embedding matrix with vectors trained with word2vec (or any other type of pre-trained embeddings: fastText, GLoVe being common) to get a perf boost. Of course, you have to ensure the embedding matrix is pre-populated with respect to the word-index mapping. Then, as you describe, these can be adjusted during training, although sometimes they're fixed. The boost is more noticeable when you're training a model on small amounts of data.