title: Is it OK to combine domain specific word2vec embeddings and off the shelf ELMo embeddings for a downstream unsupervised task?
body: <p>I am wondering if I am using word embeddings correctly.</p>
  <p>I have combined contextualised word vectors with static word vectors because:</p>
  <ul>
  <li>my domain corpus is too small to effectively train the model from scratch</li>
  <li>my domain is too specialised to use general embeddings.</li>
  </ul>
  <p>I used the off the shelf ELMo small model and trained word2vec model on a small domain specific corpus (around 500 academic papers). I then did a simple concatenation of the vectors from the two different embeddings.</p>
  <p>I loosely followed the approach in this paper:
  <a href="https://www.aclweb.org/anthology/P19-2041.pdf" rel="nofollow noreferrer">https://www.aclweb.org/anthology/P19-2041.pdf</a>
  But the approach in the paper trains the embeddings for a specific task. In my domain there is no labeled training data. Hence me just training the embeddings on the corpus alone.</p>
  <p>I am new to NLP, so apologies if I am asking a stupid question.</p>