  In practical applications of semantic parsing, we often want to rapidly
change the behavior of the parser, such as enabling it to handle queries in a
new domain, or changing its predictions on certain targeted queries. While we
can introduce new training examples exhibiting the target behavior, a mechanism
for enacting such behavior changes without expensive model re-training would be
preferable. To this end, we propose ControllAble Semantic Parser via Exemplar
Retrieval (CASPER). Given an input query, the parser retrieves related
exemplars from a retrieval index, augments them to the query, and then applies
a generative seq2seq model to produce an output parse. The exemplars act as a
control mechanism over the generic generative model: by manipulating the
retrieval index or how the augmented query is constructed, we can manipulate
the behavior of the parser. On the MTOP dataset, in addition to achieving
state-of-the-art on the standard setup, we show that CASPER can parse queries
in a new domain, adapt the prediction toward the specified patterns, or adapt
to new semantic schemas without having to further re-train the model.
