As show in (Chen et al., 2015), we take all the words of the whole sentence as the input and each token is transformed by looking up word embeddings.
we use the Skip-Gram model to pre-train the word embeddings to represent each word (Mikolov et al., 2013;Bahdanau et al., 2014).