Generating computer programs from natural language descriptions
Name
940573214-MIT.pdf
Description
Full printable version
Size
12.15 MB
Format
Adobe PDF
Checksum (MD5)
a41509eadf5faf9329ec4c0825607cc4
Author(s)
Kushman, Nate
Date Issued
2015
Publisher
Massachusetts Institute of Technology
Abstract
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.
Description
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 159-169).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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