In Word2Vec trainable model, there are two different weight matrix. The matrix $W$ from input-to-hidden layer and the matrix $W'$ from hidden-to-output layer. 

Referring to [this article][1], I understand that the reason we have the matrix $W'$ is basically to compensate for the lack of activation function in the output layer. As activation function is not needed during runtime, there is no activation function in the output layer. But we need to update the input-to-hidden layer weight matrix $W$ through backpropagation to eventually reach to the word embedding most suitable for our usecase. So there is this weight matrix $W'$ in the output layer.

But my question is why activation function is not needed during the runtime? Can anyone please explain?  


  [1]: https://datascience.stackexchange.com/questions/29019/why-do-we-need-2-matrices-for-word2vec-or-glove