<p>In short - the model certainly does use word embeddings, they are just not pre-trained embeddings like Glove or word2vec; instead, the embeddings are randomly initialised and jointly trained along with the rest of the network.</p>

<p>In the full description of the network in section A.2 of the original Bahdanau et al. paper, you'll see the word embedding matrices <code>E</code> described for both the encoder and decoder. How they were initialised is also described in section B.1.</p>

<p>This usually works as well as or better than pre-trained embeddings in situations where you have enough data. That said, in a low-resource setting, it can help to initialise the embedding matrix with pre-trained embeddings. <a href="https://arxiv.org/abs/1804.06323" rel="nofollow noreferrer">This paper</a> might help you explore that idea in further detail.</p>

<p>In addition, your statement that current implementations don't do this is not entirely accurate - while it's true that the embeddings are usually jointly trained by default, many existing neural MT toolkits have the option to initialise the embeddings with pre-trained vectors. For example, <a href="http://opennmt.net/OpenNMT-py/FAQ.html#how-do-i-use-pretrained-embeddings-e-g-glove" rel="nofollow noreferrer">OpenNMT-py</a>, <a href="https://marian-nmt.github.io/docs/#custom-embeddings" rel="nofollow noreferrer">Marian</a>.</p>
Why no word embeddings (Glove, word2vecetc) used in first attention paper?
<p>In the paper <a href="https://arxiv.org/abs/1409.0473" rel="nofollow noreferrer">Neural Machine Translation by Jointly Learning to Align and Translate Bahdanau et. al.</a> why are there no word embeddings such as Glove or word2vec used? </p>

<p>I understand that this was a 2014 paper, but the current implementations of the paper on github don't use any word embeddings as well?</p>

<p>For trying to code the paper is using word embeddings reasonable?</p>