<p>I ran into the same problem when I was trying to understand skip-gram this week. I went through which seemed to be the entire Internet without finding an answer. Fortunately I was able to figure it out.</p>

<p>Firstly, the outputs you mentioned in your question <strong>are indeed the same</strong>. You are right about that. But it still makes sense, because the reason we have say <strong>n</strong> output vectors, is that we have n words in the skip-gram window. Each output is going to be compared with a different word in this window, and we are going to compute their errors individually. Then we update the matrices with back-propagation.</p>

<p>I strongly recommend you to read this article: <a href="https://arxiv.org/pdf/1411.2738.pdf" rel="nofollow noreferrer">word2vec Parameter Learning Explained</a>. It will explain all your problems concerning the basics of word2vec.</p>

<p>Cheers!</p>
Understanding Skip gram model output
<p>If the all weight matrices between the hidden and output layer in Skip gram Word2vec model are the same, how are the outputs different one from another?</p>