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We introduce Logical Offline Cycle Consistency Optimization (LOCCO), ascalable, semi-supervised method for training a neural semantic parser.Conceptually, LOCCO can be viewed as a form of self-learning where the semanticparser being trained is used to generate annotations for unlabeled text thatare then used as new supervision. To increase the quality of annotations, ourmethod utilizes a count-based prior over valid formal meaning representationsand a cycle-consistency score produced by a neural text generation model asadditional signals. Both the prior and semantic parser are updated in analternate fashion from full passes over the training data, which can be seen asapproximating the marginalization of latent structures through stochasticvariational inference. The use of a count-based prior, frozen text generationmodel, and offline annotation process yields an approach with negligiblecomplexity and latency increases as compared to conventional self-learning. Asan added bonus, the annotations produced by LOCCO can be trivially repurposedto train a neural text generation model. We demonstrate the utility of LOCCO onthe well-known WebNLG benchmark where we obtain an improvement of 2 pointsagainst a self-learning parser under equivalent conditions, an improvement of1.3 points against the previous state-of-the-art parser, and competitive textgeneration performance in terms of BLEU score.

Scalable Learning of Latent Language Structure With Logical Offline Cycle Consistency