  Executable semantic parsing is the task of converting natural language
utterances into logical forms that can be directly used as queries to get a
response. We build a transfer learning framework for executable semantic
parsing. We show that the framework is effective for Question Answering (Q&A)
as well as for Spoken Language Understanding (SLU). We further investigate the
case where a parser on a new domain can be learned by exploiting data on other
domains, either via multi-task learning between the target domain and an
auxiliary domain or via pre-training on the auxiliary domain and fine-tuning on
the target domain. With either flavor of transfer learning, we are able to
improve performance on most domains; we experiment with public data sets such
as Overnight and NLmaps as well as with commercial SLU data. The experiments
carried out on data sets that are different in nature show how executable
semantic parsing can unify different areas of NLP such as Q&A and SLU.
