Semi-Supervised Learning with Declaratively Specified Entropy Constraints

Part of Advances in Neural Information Processing Systems 31 (NeurIPS 2018)

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Haitian Sun, William W. Cohen, Lidong Bing


We propose a technique for declaratively specifying strategies for semi-supervised learning (SSL). SSL methods based on different assumptions perform differently on different tasks, which leads to difficulties applying them in practice. In this paper, we propose to use entropy to unify many types of constraints. Our method can be used to easily specify ensembles of semi-supervised learners, as well as agreement constraints and entropic regularization constraints between these learners, and can be used to model both well-known heuristics such as co-training, and novel domain-specific heuristics. Besides, our model is flexible as to the underlying learning mechanism. Compared to prior frameworks for specifying SSL techniques, our technique achieves consistent improvements on a suite of well-studied SSL benchmarks, and obtains a new state-of-the-art result on a difficult relation extraction task.