Execution-Guided Neural Program Decoding


Jun 01, 2018 (modified: Jun 29, 2018) ICML 2018 Workshop NAMPI Blind Submission readers: everyone
  • Abstract: We present a neural semantic parser that translatesnatural language questions intoexecutableSQLqueries with two key ideas. First, we develop anencoder-decoder model, where the decoder usesa simple type system of SQL to constraint theoutput prediction, and propose a value-based losswhen copying from input tokens. Second, we ex-plore using the execution semantics of SQL to re-pair decoded programs that result in runtime erroror return empty result. We propose two model-agnostics repair approaches, an ensemble modeland a local program repair, and demonstrate theireffectiveness over the original model. We evalu-ate our model on the WikiSQL dataset and showthat our model achieves close to state-of-the-artresults with lesser model complexity.
  • Keywords: neural program synthesis, program repair, typed decoding
  • TL;DR: We present a neural semantic parser that translates natural language questions into executable SQL queries featuring a model-independent repairing module that improves decoding accuracy.
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