  Building a semantic parser quickly in a new domain is a fundamental challenge
for conversational interfaces, as current semantic parsers require expensive
supervision and lack the ability to generalize to new domains. In this paper,
we introduce a zero-shot approach to semantic parsing that can parse utterances
in unseen domains while only being trained on examples in other source domains.
First, we map an utterance to an abstract, domain-independent, logical form
that represents the structure of the logical form, but contains slots instead
of KB constants. Then, we replace slots with KB constants via lexical alignment
scores and global inference. Our model reaches an average accuracy of 53.4% on
7 domains in the Overnight dataset, substantially better than other zero-shot
baselines, and performs as good as a parser trained on over 30% of the target
domain examples.
