  Interacting with computers is a ubiquitous activity for millions of people.
Repetitive or specialized tasks often require creation of small, often one-off,
programs. End-users struggle with learning and using the myriad of
domain-specific languages (DSLs) to effectively accomplish these tasks.
  We present a general framework for constructing program synthesizers that
take natural language (NL) inputs and produce expressions in a target DSL. The
framework takes as input a DSL definition and training data consisting of
NL/DSL pairs. From these it constructs a synthesizer by learning optimal
weights and classifiers (using NLP features) that rank the outputs of a
keyword-programming based translation. We applied our framework to three
domains: repetitive text editing, an intelligent tutoring system, and flight
information queries. On 1200+ English descriptions, the respective synthesizers
rank the desired program as the top-1 and top-3 for 80% and 90% descriptions
respectively.
