The goal of program synthesis from examples is to find a computer program that is consistent with a given set of input-output examples. Most learning-based approaches try to find a program that satisfies all examples at once. Our work, by contrast, considers an approach that breaks the problem into two stages: (a) find programs that satisfy only one example, and (b) leverage these per-example solutions to yield a program that satisfies all examples. We introduce the Cross Aggregator neural network module based on a multi-head attention mechanism that learns to combine the cues present in these per-example solutions to synthesize a global solution. Evaluation across programs of different lengths and under two different experimental settings reveal that when given the same time budget, our technique significantly improves the success rate over PCCoder arXiv:1809.04682v2 [cs.LG] and other ablation baselines. The code, data and trained models for our work can be found at https://github.com/shrivastavadisha/N-PEPS.

中文翻译：

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学习结合每个示例的神经程序综合解决方案

从示例中合成程序的目标是找到与给定的输入输出示例集一致的计算机程序。大多数基于学习的方法都试图找到一个同时满足所有示例的程序。相比之下，我们的工作考虑了一种将问题分解为两个阶段的方法：(a) 找到仅满足一个示例的程序，以及 (b) 利用这些每个示例的解决方案来生成满足所有示例的程序。我们引入了基于多头注意力机制的 Cross Aggregator 神经网络模块，该模块学习组合这些每个示例解决方案中存在的线索以合成全局解决方案。对不同长度的项目和在两种不同的实验环境下的评估表明，当给定相同的时间预算时，我们的技术显着提高了 PCCoder arXiv:1809.04682v2 [cs.LG] 和其他消融基线的成功率。我们工作的代码、数据和训练模型可以在 https://github.com/shrivastavadisha/N-PEPS 上找到。