Leveraging Language to Learn Program Abstractions and Search Heuristics
Catherine Wong,u00a0Kevin M Ellis,u00a0Joshua Tenenbaum,u00a0Jacob Andreas
Inductive program synthesis, or inferring programs from examples of desired behavior, offers a general paradigm for building interpretable, robust, andgeneralizable machine learning systems. Effective program synthesis depends on two key ingredients: a strong library of functions from which to build programs, and an efficient search strategy for finding programs that solve a given task. We introduce LAPS (Language for Abstraction and Program Search), a technique for using natural language annotations to guide joint learning of libraries and neurally-guided search models for synthesis. When integrated into a state-of-the-art library learning system (DreamCoder), LAPS produces higher-quality libraries and improves search efficiency and generalization on three domains {u2013} string editing, image composition, and abstract reasoning about scenes {u2013} even when no natural language hints are available at test time.
