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Interesting. The non-negative matrix factorization on the first level kinda highlight some semantic groupings : paragraph, verbs, auxiliaries, commas, pronouns, nominal propositions. I tried to look at higher level layers, and the grouping were indeed of higher level : for example at level 4 there was a grouping which highlighted for any punctuation (and not just comma). The grouping were also qualifying more : for example ("would deliberately" whereas at lower level it was just would). But it's not as clear as I had hoped it would be. I hoped it would somehow highlight grouping of higher and higher size, that could nicely map to the equivalent of a parse-tree. The problem I have with this kind of visualizations, is that they often require interpretation. Also, they don't tell me if the structure was really present by the neural network but was just not apparent because the prism of the Non-negative Matrix Factorization hid it. For my own networks, instead of visualizing, I like to quantify things a little more. I give the neural network some additional layers, and I try to make the neural network produce the visualization directly. I give it some examples of what I'd like the visualization to look like, and jointly train/fine-tune the neural network so that it solve simultaneously his original task, and the production of the visualization which is then easier to inspect. Depending on how many additional layers I had to add, and depending on where they were added, and depending on how accurate (measured by a Loss Function!) the network prediction are, I can better infer how it's working internally, and whether or not the network is really doing the work or if it is taking some mental shortcuts. For example in my Colorify [1] browser extension, which aims to reduce the cognitive load of reading, I use neural networks to predict simultaneously visualizations of sentence-grouping, linguistic features, and even the parse-tree. [1] https://addons.mozilla.org/en-US/firefox/addon/colorify/ |