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RobustFill: Neural Program Learning under Noisy I/O
Jacob Devlin · Jonathan Uesato · Surya Bhupatiraju · Rishabh Singh · Abdelrahman Mohammad · Pushmeet Kohli

Tue Aug 08 01:30 AM -- 05:00 AM (PDT) @ Gallery #118

The problem of automatically generating a computer program from some specification has been studied since the early days of AI. Recently, two competing approaches for 'automatic program learning' have received significant attention: (1) 'neural program synthesis', where a neural network is conditioned on input/output (I/O) examples and learns to generate a program, and (2) 'neural program induction', where a neural network generates new outputs directly using a latent program representation. Here, for the first time, we directly compare both approaches on a large-scale, real-world learning task and we additionally contrast to rule-based program synthesis, which uses hand-crafted semantics to guide the program generation. Our neural models use a modified attention RNN to allow encoding of variable-sized sets of I/O pairs, which achieve 92% accuracy on a real-world test set, compared to the 34% accuracy of the previous best neural synthesis approach. The synthesis model also outperforms a comparable induction model on this task, but we more importantly demonstrate that the strength of each approach is highly dependent on the evaluation metric and end-user application. Finally, we show that we can train our neural models to remain very robust to the type of noise expected in real-world data (e.g., typos), while a highly-engineered rule-based system fails entirely.

Author Information

Jacob Devlin (Microsoft Research)
Jonathan Uesato (MIT)
Surya Bhupatiraju (MIT)
Rishabh Singh (Microsoft Research)
Abdelrahman Mohammad (Microsoft)
Pushmeet Kohli (Microsoft Research)

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