  Semantic parsing is the task of converting natural language utterances to
machine-understandable meaning representations, such as logic forms or
programming languages. Training datasets for semantic parsing are typically
small due to the higher expertise required for annotation than most other NLP
tasks. As a result, models for this application usually require additional
prior knowledge to be built into the architecture or algorithm. The increased
dependency on human experts hinders automation and raises the development and
maintenance costs in practice. This work investigates whether a generic
transformer-based seq2seq model can achieve competitive performance with
minimal semantic-parsing specific inductive bias design. By exploiting a
relatively large monolingual corpus of the target programming language, which
is cheap to mine from the web, unlike a parallel corpus, we achieved 80.75%
exact match accuracy on Django and 32.57 BLEU score on CoNaLa, both are SOTA to
the best of our knowledge. This positive evidence highlights a potentially
easier path toward building accurate semantic parsers in the wild.
