论文标题
通过神经程序综合的进化来创建合成数据集
Creating Synthetic Datasets via Evolution for Neural Program Synthesis
论文作者
论文摘要
程序合成是自动生成与给定规范一致的程序的任务。指定程序的一种自然方法是提供所需的输入输出行为的示例，并且在随机生成的输入输出示例训练后，许多当前的程序合成方法在训练后取得了令人印象深刻的结果。但是，最近的工作发现，其中一些方法对数据分布的推广不佳，与随机生成的示例不同。我们表明，此问题也适用于其他最先进的方法，而解决此问题的当前方法不足。然后，我们提出了一种新的，对抗性的方法来控制合成数据分布的偏见，并表明它表现优于当前方法。
Program synthesis is the task of automatically generating a program consistent with a given specification. A natural way to specify programs is to provide examples of desired input-output behavior, and many current program synthesis approaches have achieved impressive results after training on randomly generated input-output examples. However, recent work has discovered that some of these approaches generalize poorly to data distributions different from that of the randomly generated examples. We show that this problem applies to other state-of-the-art approaches as well and that current methods to counteract this problem are insufficient. We then propose a new, adversarial approach to control the bias of synthetic data distributions and show that it outperforms current approaches.
