This is a great article. One big thing NLP lacks (soon enough, lacked) over computer vision is its own version of ImageNet and other similar off the shelf model

This is a great article. One big thing NLP lacks (soon enough, lacked) over computer vision is its own version of ImageNet and other similar off the shelf models that can be fit to different domains. Many NLP models are brittle due to the disparity between tasks and problem domains. My BiLSTM-CRF model for NER would require retraining on a completely different set of labels in order to run inference on other tasks. Elmo embeddings are especially interesting. Dynamic Bernoulli embeddings also deserve a mention. https://arxiv.org/pdf/1703.08052.pdf https://arxiv.org/pdf/1703.08052.pdf