  While neural networks have shown impressive performance on large datasets,
applying these models to tasks where little data is available remains a
challenging problem.
  In this paper we propose to use feature transfer in a zero-shot experimental
setting on the task of semantic parsing.
  We first introduce a new method for learning the shared space between
multiple domains based on the prediction of the domain label for each example.
  Our experiments support the superiority of this method in a zero-shot
experimental setting in terms of accuracy metrics compared to state-of-the-art
techniques.
  In the second part of this paper we study the impact of individual domains
and examples on semantic parsing performance.
  We use influence functions to this aim and investigate the sensitivity of
domain-label classification loss on each example.
  Our findings reveal that cross-domain adversarial attacks identify useful
examples for training even from the domains the least similar to the target
domain. Augmenting our training data with these influential examples further
boosts our accuracy at both the token and the sequence level.
