一句话：提出一个从程序空间描述、模型架构与结果处理三个维度改进神经程序合成的框架，提升合成规模与正确性。
神经程序合成通常采用编码器-解码器模型，学习程序空间上的概率分布。然而这类方法存在两个缺陷：合成规模相对较小，且合成结果的正确性无法得到保证。本文构建了一个框架，从三个维度对上述问题进行分析与求解：程序空间描述、模型架构以及结果处理。在程序空间描述维度，改进对搜索空间的刻画；在模型架构维度，调整网络结构以提升可扩展性；在结果处理维度，对模型输出进行加工以提高正确性。实验结果表明，该框架在每一个维度上都提升了合成的可扩展性与精度，说明多维度协同改进是缓解神经程序合成规模受限与正确性不足问题的有效途径。
Mosts of the neural synthesis construct encoder-decoder models to learn a probability distribution over the space of programs. Two drawbacks in such neural program synthesis are that the synthesis scale is relatively small and the correctness of the synthesis result cannot be guaranteed. We address these problems by constructing a framework, which analyzes and solves problems from three dimensions: program space description, model architecture, and result processing. Experiments show that the scalability and precision of synthesis are improved in every dimension.
DOI 原文 ·
@inproceedings{paperbot5896,
title = {Scalability and precision improvement of neural program synthesis},
author = {Yating Zhang},
booktitle = {ASE},
year = {2020},
doi = {10.1145/3324884.3418912}
}