Abstract
We consider a zero-shot semantic parsing task:
parsing instructions into compositional logical
forms, in domains that were not seen during
training. We present a new dataset with 1,390
examples from 7 application domains (e.g. a
calendar or a file manager), each example consisting of a triplet: (a) the application’s initial
state, (b) an instruction, to be carried out in
the context of that state, and (c) the state of
the application after carrying out the instruction. We introduce a new training algorithm
that aims to train a semantic parser on examples from a set of source domains, so that it
can effectively parse instructions from an unknown target domain. We integrate our algorithm into the floating parser of Pasupat and
Liang (2015), and further augment the parser
with features and a logical form candidate filtering logic, to support zero-shot adaptation.
Our experiments with various zero-shot adaptation setups demonstrate substantial performance gains over a non-adapted parser.1