"Update by Richard Socher (Nov 2014): This document is outdated and its concerns have been addressed in the final version of the GloVe paper. Glove gets better performance on the same training data when actually run to convergence. See last section of Glove paper for details."

This is a good example of peer review in academia beyond just the paper review committee -- other researchers point out concerns or issues with methodology and they're addressed by the authors or other contributors. It's also great that the initial concerns could be properly tested thanks to the open source nature of both projects.

I will admit I didn't discuss the intricacies of the evaluation in my few paragraphs above, I was primarily speaking to the broader point that open data is helping academia compete with the goliaths of industrial research! =]

As I said in my other comment, one of the strengths of Word2Vec is how robust it is against various metrics.

While it looks like GloVe's advantages over Word2Vec may be not as much as initially claimed, it is mostly as robust (which is good). However, the jump in Word+Context over just Word vectors when evaluated on semantic relations is interesting.

(To be clear: I'm very interested be being able to use the same system over diverse datasets, without having to tune it differently for each system - hence my interest in the robustness of the methodologies)

Edit: Were you and Smerity at Sydney Uni at the same time?