  Semantic parsing plays a key role in digital voice assistants such as Alexa,
Siri, and Google Assistant by mapping natural language to structured meaning
representations. When we want to improve the capabilities of a voice assistant
by adding a new domain, the underlying semantic parsing model needs to be
retrained using thousands of annotated examples from the new domain, which is
time-consuming and expensive. In this work, we present an architecture to
perform such domain adaptation automatically, with only a small amount of
metadata about the new domain and without any new training data (zero-shot) or
with very few examples (few-shot). We use a base seq2seq (sequence-to-sequence)
architecture and augment it with a concept encoder that encodes intent and slot
tags from the new domain. We also introduce a novel decoder-focused approach to
pretrain seq2seq models to be concept aware using Wikidata and use it to help
our model learn important concepts and perform well in low-resource settings.
We report few-shot and zero-shot results for compositional semantic parsing on
the TOPv2 dataset and show that our model outperforms prior approaches in
few-shot settings for the TOPv2 and SNIPS datasets.
