Generating computer programs from natural language descriptions
2015
This thesis addresses the problem of learning to
translate natural language into preexisting programming languages
supported by widely-deployed computer systems. Generating programs
for existing computer systems enables us to take advantage of two
important capabilities of these systems: computing the semantic
equivalence between programs, and executing the programs to obtain
a result. We present probabilistic models and inference algorithms
which integrate these capabilities into the learning process. We
use these to build systems that learn to generate programs from
natural language in three different computing domains: text
processing, solving math problems, and performing robotic tasks in
a virtual world. In all cases the resulting systems provide
significant performance gains over strong baselines which do not
exploit the underlying system capabilities to help interpret the
text.
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