  Semantic parsing is the process of translating natural language utterances
into logical forms, which has many important applications such as question
answering and instruction following. Sequence-to-sequence models have been very
successful across many NLP tasks. However, a lack of task-specific prior
knowledge can be detrimental to the performance of these models. Prior work has
used frameworks for inducing grammars over the training examples, which capture
conditional independence properties that the model can leverage. Inspired by
the recent success stories such as BERT we set out to extend this augmentation
framework into two stages. The first stage is to pre-train using a corpus of
augmented examples in an unsupervised manner. The second stage is to fine-tune
to a domain-specific task. In addition, since the pre-training stage is
separate from the training on the main task we also expand the universe of
possible augmentations without causing catastrophic inference. We also propose
a novel data augmentation strategy that interchanges tokens that co-occur in
similar contexts to produce new training pairs. We demonstrate that the
proposed two-stage framework is beneficial for improving the parsing accuracy
in a standard dataset called GeoQuery for the task of generating logical forms
from a set of questions about the US geography.
