We propose Object-oriented Neural Programming (OONP), a framework for semantically parsing documents in specific domains. Basically, OONP reads a document and parses it into a predesigned object-oriented data structure (referred to as ontology in this paper) that reflects the domain-specific semantics of the document. An OONP parser models semantic parsing as a decision process: a neural net-based Reader sequentially goes through the document, and during the process it builds and updates an intermediate ontology to summarize its partial understanding of the text it covers. OONP supports a rich family of operations (both symbolic and differentiable) for composing the ontology, and a big variety of forms (both symbolic and differentiable) for representing the state and the document. An OONP parser can be trained with supervision of different forms and strength, including supervised learning (SL) , reinforcement learning (RL) and hybrid of the two. Our experiments on both synthetic and real-world document parsing tasks have shown that OONP can learn to handle fairly complicated ontology with training data of modest sizes.

本文介绍了OONP框架——面向对象的神经编程，用于将特定领域的文档进行语义分析并将其解析为反映文档领域特定语义的预定义面向对象数据结构。OONP解析器通过神经网络模型将语义分析建模为决策过程；在读取文档时会逐步构建和更新中间本体以总结对其覆盖文本的部分理解，并支持一系列符号和可微分的操作以及符号和可微分的列表形式。OONP解析器可以受不同形式和强度的监督训练，包括监督学习（SL）、强化学习（RL）和两者混合。实验结果表明，OONP可以学会处理使用中等规模的训练数据生成的相当复杂的本体。

面向对象的神经编程（OONP）用于文档理解