• The most dramatic performance gain comes from discrete embedding dropout: You embed as usual, but now with a probability p you zero the entire word vector. This is akin to masked language modeling but the goal is not to predict the mask — just regular LM with uncertain context. • The second most important factor is regular input dropout: You take the embeddings and dropout elements with probability p. This also has a data augmentation effect very similar to dropping out random pixels for images. What is a good way to think about this?
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