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Our goal is to enable machine learning systems to be trained interactively.This requires models that perform well and train quickly, without large amountsof hand-labeled data. We take a step forward in this direction by borrowingfrom weak supervision (WS), wherein models can be trained with noisy sources ofsignal instead of hand-labeled data. But WS relies on training downstream deepnetworks to extrapolate to unseen data points, which can take hours or days.Pre-trained embeddings can remove this requirement. We do not use theembeddings as features as in transfer learning (TL), which requires fine-tuningfor high performance, but instead use them to define a distance function on thedata and extend WS source votes to nearby points. Theoretically, we provide aseries of results studying how performance scales with changes in sourcecoverage, source accuracy, and the Lipschitzness of label distributions in theembedding space, and compare this rate to standard WS without extension and TLwithout fine-tuning. On six benchmark NLP and video tasks, our methodoutperforms WS without extension by 4.1 points, TL without fine-tuning by 12.8points, and traditionally-supervised deep networks by 13.1 points, and comeswithin 0.7 points of state-of-the-art weakly-supervised deep networks-all whiletraining in less than half a second.

Train and You'll Miss It: Interactive Model Iteration with Weak Supervision and Pre-Trained Embeddings