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.

本研究发现目前基于随机生成数据的程序综合方法面对数据分布改变的情况下普遍存在泛化能力不足的问题，提出了一种新的对抗式方法，用于控制合成数据分布的偏差从而得到更好的结果。

通过进化的方式为神经程序合成创建合成数据集

Creating Synthetic Datasets via Evolution for Neural Program Synthesis

The goal of program synthesis is to automatically generate programs in a
particular language from corresponding specifications, e.g. input-output
behavior. Many current approaches achieve impressive results after training on
randomly generated I/O examples in limited domain-specific languages (DSLs), as
with string transformations in RobustFill. However, we empirically discover
that applying test input generation techniques for languages with control flow
and rich input space causes deep networks to generalize poorly to certain data
distributions; to correct this, we propose a new methodology for controlling
and evaluating the bias of synthetic data distributions over both programs and
specifications. We demonstrate, using the Karel DSL and a small Calculator DSL,
that training deep networks on these distributions leads to improved
cross-distribution generalization performance.

本文旨在探索自动生成指定语言中的程序，依据输入输出行为生成程序，我们提出了一种新的方法来控制和评估合成数据分布的偏差，通过在 Karel 和小型计算器 DSL 上的实验表明，使用这些分布训练深度网络可以提高跨分布的泛化性能。