  We introduce Logical Offline Cycle Consistency Optimization (LOCCO), a
scalable, semi-supervised method for training a neural semantic parser.
Conceptually, LOCCO can be viewed as a form of self-learning where the semantic
parser being trained is used to generate annotations for unlabeled text that
are then used as new supervision. To increase the quality of annotations, our
method utilizes a count-based prior over valid formal meaning representations
and a cycle-consistency score produced by a neural text generation model as
additional signals. Both the prior and semantic parser are updated in an
alternate fashion from full passes over the training data, which can be seen as
approximating the marginalization of latent structures through stochastic
variational inference. The use of a count-based prior, frozen text generation
model, and offline annotation process yields an approach with negligible
complexity and latency increases as compared to conventional self-learning. As
an added bonus, the annotations produced by LOCCO can be trivially repurposed
to train a neural text generation model. We demonstrate the utility of LOCCO on
the well-known WebNLG benchmark where we obtain an improvement of 2 points
against a self-learning parser under equivalent conditions, an improvement of
1.3 points against the previous state-of-the-art parser, and competitive text
generation performance in terms of BLEU score.

Computation and Language;Artificial Intelligence