[andreyk]

I have finished a PhD in AI just this past year, and can assure you there exist reviewers who spend hours per review to do it well. It's true that these days it's often the case that you can (and are more likely than not to) get unlucky with lazier reviewers, but that does not appear to have been the case with this paper.

For example just see this from the review of f5bf:

"The main contribution of the paper comprises two new NLM architectures that facilitate training on massive data sets. The first model, CBOW, is essentially a standard feed-forward NLM without the intermediate projection layer (but with weight sharing + averaging before applying the non-linearity in the hidden layer). The second model, skip-gram, comprises a collection of simple feed-forward nets that predict the presence of a preceding or succeeding word from the current word. The models are trained on a massive Google News corpus, and tested on a semantic and syntactic question-answering task. The results of these experiments look promising.

...

(2) The description of the models that are developed is very minimal, making it hard to determine how different they are from, e.g., the models presented in [15]. It would be very helpful if the authors included some graphical representations and/or more mathematical details of their models. Given that the authors still almost have one page left, and that they use a lot of space for the (frankly, somewhat superfluous) equations for the number of parameters of each model, this should not be a problem."

These reviews in turn led to significant (though apparently not significant enough) modifications to the paper (https://openreview.net/forum?id=idpCdOWtqXd60&noteId=C8Vn84f...). These were some quality reviews and the paper benefited from going this review process, IMHO.