The meaning of random word dropout in NLP
It is not uncommon that we can make sense of a sentence without reading it completely. Or when you are having a quick look at a document, you tend to oversee some words and still understand the main point. This is the intuition behind the word dropout.

Generally this is done by randomly dropping each word in a sequence following for example a Bernoulli distribution:


  $X \leftarrow X \odot \vec{e}, \vec{e} ∼ B(n, p)$


where X is the index of the word token, n
is the lenth of the sequence, and
 $\vec{e}$ is a vector with each word dropout state.

This is usually done after calculating the word embeddings, and the words selected to be left out are normally changed to the &lt;UNK&gt; equivalent embedding. 

By doing this, we allow out model to learn more flexible ways of writing/convey meaning.