Great slideshow. Word embeddings are already indispensable in NLP, but I'm wondering if it is right to assign just a unique vector per word. Instead, each word should have a vector based on context, for disambiguation and nuance.

I see word meaning as an action similar to object recognition in vision - we have to infer meaning because the actual word itself is a family of meanings. Most of the time it's used one word - one embedding, even in word2vec which is the standard method used in papers.

Of course word sense disambiguation is an old topic of research and there are many methods. The translation task is probably the ultimate word-disambiguation application - it shows how much work is still to do after word embedding, in order to understand the meaning of words. If words had unique meanings, we could have done translation with simple dictionary substitutions.