Concerns about interpretability, computational resources, and principled inductive priors have motivated efforts to engineer sparse  neural  models for NLP tasks. If sparsity is important for NLP, might well-trained neural models naturally become roughly sparse? Using the Taxi-Euclidean norm to measure sparsity, we find that frequent input words are associated with concentrated or sparse activations, while frequent target words are associated with dispersed activations but concentrated gradients. We find that  gradients associated with function words are more concentrated than the gradients of content words, even controlling for word frequency. Researchers in NLP have long relied on engineering features to reflect the sparse structures underlying language. Modern deep learning methods promised to relegate this practice to history, but have not eliminated the interest in sparse modeling for NLP. Along with concerns about computational resources BID0 BID12 and interpretability BID10 BID21 , human intuitions continue to motivate sparse representations of language. For example, some work applies assumptions of sparsity to model latent hard categories such as syntactic dependencies BID14 or phonemes BID1 . BID13 found that a sparse attention mechanism outperformed dense methods on some NLP tasks; BID11 found sparsified versions of LMs that outperform dense originals. Attempts to engineer sparsity rest on an unstated assumption that it doesn't arise naturally when neural models are learned. Is this true?Using a simple measure of sparsity, we analyze how it arises in different layers of a neural language model in relation to word frequency. We show that the sparsity of a word representation increases with exposure to that word during training. We also find evidence of syntactic learning: gradient updates in backpropagation depend on whether a word's part of speech is open or closed class, even controlling for word frequency.
We study the natural emergence of sparsity in the activations and gradients for some layers of a dense LSTM language model, over the course of training.
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