In this model, we directly use the sense selected for each token by one of the WSD systems above, and use the embeddings of the respective sense as generated by NMT after training.
it appeared that learning sense embeddings for NMT is better than using embeddings learned separately by other methods, although such embeddings may be useful for initialization.