I stumbled upon this paper "Reflections after Refereeing Papers for NIPS" by Leo Breiman that gives some really candid insights into theory papers. (Unfortunately, I could not find a soft copy to share, except this link.) Some noteworthy observations:

"No theorems" implies "No theory"

"... more than 99% of the published papers are useless exercises."

"Mathematical theory is not critical to development of machine learning."

"Our fields would be better off with far fewer theorems, less emphasis on faddish stuff, and much more into scientific inquiry and engineering."

I really liked this article, especially coming from someone who has been working in theory all his life but I would still prefer reading papers giving theoretical insight, however useless, than pages and pages of feature engineering & experimentation using classifier X on problem Y -- the current trend at ACL.

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Many institutions limit access to their online information. Making this information available will be an asset to all.

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