word2vec: CBOW & skip-gram performance wrt training dataset size
<p>The question is simple. Which of the CBOW &amp; skip-gram works better for a big dataset? (And the answer for small dataset follows.)</p>

<p>I am confused since, by Mikolov himself, <a href="https://groups.google.com/forum/#!searchin/word2vec-toolkit/c-bow/word2vec-toolkit/NLvYXU99cAM/E5ld8LcDxlAJ" rel="noreferrer">[Link]</a></p>

<blockquote>
  <p>Skip-gram: works well with <strong>small amount of the training data</strong>, represents well even rare words or phrases. <br/> <br/>
  CBOW: several times faster to train than the skip-gram, slightly better accuracy for the frequent words</p>
</blockquote>

<p>but, according to Google TensorFlow, <a href="https://www.tensorflow.org/versions/r0.10/tutorials/word2vec/index.html" rel="noreferrer">[Link]</a></p>

<blockquote>
  <p>CBOW smoothes over a lot of the distributional information (by treating an entire context as one observation). For the most part, this turns out to be a useful thing for smaller datasets.<br/><br/>However, skip-gram treats each context-target pair as a new observation, and this tends to do better when we have <strong>larger datasets</strong>. We will focus on the skip-gram model in the rest of this tutorial.</p>
</blockquote>

<p>Here is a Quora post which supports the first thought <a href="https://www.quora.com/What-are-the-continuous-bag-of-words-and-skip-gram-architectures-in-laymans-terms" rel="noreferrer">[Link]</a>, and then there is the other Quora post which suggests the second thought <a href="https://www.quora.com/What-are-the-continuous-bag-of-words-and-skip-gram-architectures-in-laymans-terms" rel="noreferrer">[Link]</a>--both seem derivable from the aforementioned credible sources.</p>

<p>Or is it like what Mikolov said:</p>

<blockquote>
  <p>Overall, the best practice is to try few experiments and see what works the best for you, as different applications have different requirements.</p>
</blockquote>

<p>But surely there is an empirical or analytical verdict or final saying on this matter?</p>