  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 need 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 code-generation-specific
inductive bias design. By exploiting a relatively sizeable monolingual corpus
of the target programming language, which is cheap to mine from the web, we
achieved 81.03% 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 practice.
