The Unreasonable Ineffectiveness of Deep Learning in NLU

I often get pitched with a superior deep learning solution for Natural Language Understanding (NLU). After all, deep learning is the disruptive new force in AI. A better NLU AI entices many useful advancements, ranging from smarter chat bots and virtual assistants to news categorization, with an ultimate promise of better language comprehension. Lets assume this superior deep learning (DL) "product" is called "(dot)AI". Their pitch deck will invariably have a bar chart that looks something like this -- the claim being that the new DL topic classifier/tagger of (Dot)AI is better than state of the art methods.