Simplifying Models with Unlabeled Output Data

29 Jun 2020Sang Michael XieTengyu MaPercy Liang

We focus on prediction problems with high-dimensional outputs that are subject to output validity constraints, e.g. a pseudocode-to-code translation task where the code must compile. For these problems, labeled input-output pairs are expensive to obtain, but "unlabeled" outputs, i.e. outputs without corresponding inputs, are freely available and provide information about output validity (e.g. code on GitHub)... (read more)

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