Self-Supervised Self-Supervision by Combining Deep Learning and Probabilistic Logic

Labeling training examples at scale is a perennial challenge in machine learning. Self-supervision methods compensate for the lack of direct supervision by leveraging prior knowledge to automatically generate noisy labeled examples. Deep probabilistic logic (DPL) is a unifying framework for selfsupervised learning that represents unknown labels as latent variables and incorporates diverse self-supervision using probabilistic logic to train a deep neural network end-toend using variational EM. While DPL is successful at combining pre-specified self-supervision, manually crafting selfsupervision to attain high accuracy may still be tedious and challenging. In this paper, we propose Self-Supervised Self- Supervision (S4), which adds to DPL the capability to learn new self-supervision automatically. Starting from an initial “seed, ” S4 iteratively uses the deep neural network to propose new self supervision. These are either added directly (a form of structured self-training) or verified by a human expert (as in feature-based active learning). Experiments show that S4 is able to automatically propose accurate self-supervision and can often nearly match the accuracy of supervised methods with a tiny fraction of the human effort.

Authors

• Hunter Lang
• Hoifung Poon

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