title: Neural-Guided Deductive Search for Real-Time Program Synthesis from Examples
abstract: Synthesizing user-intended programs from a small number of input-output exam-
  ples is a challenging problem with several important applications like spreadsheet
  manipulation, data wrangling and code refactoring. Existing synthesis systems
  either completely rely on deductive logic techniques that are extensively hand-
  engineered or on purely statistical models that need massive amounts of data, and in
  general fail to provide real-time synthesis on challenging benchmarks. In this work,
  we propose Neural Guided Deductive Search (NGDS), a hybrid synthesis technique
  that combines the best of both symbolic logic techniques and statistical models.
  Thus, it produces programs that satisfy the provided specifications by construction
  and generalize well on unseen examples, similar to data-driven systems. Our
  technique effectively utilizes the deductive search framework to reduce the learning
  problem of the neural component to a simple supervised learning setup. Further,
  this allows us to both train on sparingly available real-world data and still leverage
  powerful recurrent neural network encoders. We demonstrate the effectiveness
  of our method by evaluating on real-world customer scenarios by synthesizing
  accurate programs with up to 12× speed-up compared to state-of-the-art systems.
authors: Ashwin Kalyan Abhishek Mohta Oleksandr Polozov Dhruv Batra Prateek Jain Sumit Gulwani
hIndex: Dhruv Batra 63