Leveraging Language to Learn Program Abstractions and Search Heuristics

• URL: http://arxiv.org/abs/2106.11053v1
• Date: Fri, 18 Jun 2021 15:08:47 GMT
• Title: Leveraging Language to Learn Program Abstractions and Search Heuristics
• Authors: Catherine Wong and Kevin Ellis and Joshua B. Tenenbaum and Jacob Andreas
• Abstract summary: 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.
• Score: 66.28391181268645
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Inductive program synthesis, or inferring programs from examples of desired behavior, offers a general paradigm for building interpretable, robust, and generalizable 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 -- string editing, image composition, and abstract reasoning about scenes -- even when no natural language hints are available at test time.

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