The speaker prompt is the sample speaker voice reading a random text, that’s one piece that the model uses as input. The second column corresponds to the human speaker reading the text (ground truth) The two next columns are baseline and VALL-E producing text-to-speech respectively, given the first column and only the text as input.
What’s the study’s control group? If we compare the number of companies founded by students over different time periods it doesn’t say much about the effects of the “brain drain”.
The argument the author uses is invalid. NNs are equally strong at modelling sparse signals, provided that they could be mapped into a continuous space, what is commonly referred to as an 'embedding'.
The premise of the article is valid though, in that NLP is a hard problem. The reason is partly because NLP is ill-defined; how do you define language understanding?
NNs are very effective at learning mappings of y=f(X), given enough examples. One of the reasons that they're so effective at modelling speech, vision, translation, etc., is that such mappings exist in high volumes. Because of the above-mentioned ambiguity of NLP, it's harder to come up with such pairs for 'understanding' a language. How do you come up with a dataset of sentences and their 'meaning'? Probably the best you could do is to map them to some action. And critics will readily disregard such attempts as 'not really NLP'.
The problem with deep learning and language understanding is that the task is ill-defined end-to-end. For speech, image understanding, and translation, you can come up with large datasets of x->y and have deep learning learn a complex function to approximate the mapping. We don't have that luxury in language understanding, at least not yet.