Program Synthesis: Does Feedback Help?

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Computers are devices that execute precise instructions provided to them using various programming languages. However, the idea of delivering instructions to a computer through natural language could vastly simplify the act of programming as a specific task. Generating code from high-level descriptions for a given program is a significantly challenging task and has been an active area of research in the natural language processing domain. In this paper, we present a novel feedback-based deep learning approach for synthesizing code from human-specified descriptions. Inspired by the dual-learning mechanism, our framework uses a feedback loss to produce more consistent and robust predictions. We show how our approach fares empirically on standard code generation datasets and achieves state-of-the-art results on the NAPS (Natural Program Synthesis) dataset.

Recommended citation: Harsh Patel*, Praveen Venkatesh*, Shivam Sahni*, Varun Jain*, Mrinal Anand, Mayank Singh. "Program Synthesis: Does Feedback Help?." ACM India 5th Joint International Conference on Data Science and Management of Data [CODS-COMAD 2022].
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