if Word2Vec is nothing but a transformation of one-hot into a dense vector, why can't I just feed one-hot into LSTM (or for that matter sacrifice first dense layer, in any network that will end up using the embedding) and call it a day?

Why would I actually spend time pre-computing Word2Vec embeddings? Yes, the resulting embedings are vectors that are clustered together if the words have similar meaning. But I presume a feed-forward classifier would figure out a good internal model anyway?


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You don't need word embeddings. Actually, in neural machine translation is frequent not to use them and simply train the embeddings along with the task.

Nevertheless, word embeddings work as a data augmentation technique, as you normally use a different (and much larger) dataset to train them, so they can be useful when you don't have much training data.

Therefore, the decision of using pre-trained word embeddings or not should be driven by the available data.


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