Automatic translation from natural language descriptions into programs is a
longstanding challenging problem. In this work, we consider a simple yet
important sub-problem: translation from textual descriptions to If-Then
programs. We devise a novel neural network architecture for this task which we
train end-to-end. Specifically, we introduce Latent Attention, which computes
multiplicative weights for the words in the description in a two-stage process
with the goal of better leveraging the natural language structures that
indicate the relevant parts for predicting program elements. Our architecture
reduces the error rate by 28.57% compared to prior art. We also propose a
one-shot learning scenario of If-Then program synthesis and simulate it with
our existing dataset. We demonstrate a variation on the training procedure for
this scenario that outperforms the original procedure, significantly closing
the gap to the model trained with all data.

利用神经网络架构中的潜在注意力机制实现翻译从自然语言描述到 if-then 程序的自动翻译，然后探讨在 one-shot 学习场景下的 if-then 程序综合模拟，并提出了一种改进的训练过程，进一步提高了模型的性能表现。