Object-oriented Neural Programming (OONP) for Document Understanding
Zhengdong Lu, Xianggen Liu, Haotian Cui, Yukun Yan, Daqi Zheng · 2018
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 that reflects the domain-specific semantics of the document.An OONP parser models semantic parsing as a decision process: a neural netbased Reader sequentially goes through the document, and builds and updates an intermediate ontology during the process to summarize its partial understanding of the text.OONP supports a big variety of forms (both symbolic and differentiable) for representing the state and the document, and a rich family of operations to compose the representation.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.* The work was done when these authors worked as interns at DeeplyCurious.ai.