We consider the problem of translating high-level textual descriptions to formal representations in technical documentation as part of an effort to model the meaning of such documentation. We focus specifically on the problem of learning translational correspondences between text descriptions and grounded representations in the target documentation, such as formal representation of functions or code templates. Our approach exploits the parallel nature of such documentation, or the tight coupling between high-level text and the low-level representations we aim to learn. Data is collected by mining technical documents for such parallel text-representation pairs, which we use to train a simple semantic parsing model. We report new baseline results on sixteen novel datasets, including the standard library documentation for nine popular programming languages across seven natural languages, and a small collection of Unix utility manuals.

本文通过挖掘技术文档中的文本-表达式对来训练一个基于语义分析的模型，以学习高级文本描述和目标文档中表现函数或代码模板的形式表示之间的翻译对应关系，并在 16 个新颖数据集上报告了新的基线结果，包括 7 种自然语言的九种流行编程语言的标准库文档和一些UNIX实用程序手册。

学习技术文档中的语义关联