  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.
