Joint Structured Learning and Predictions under Logical Constraints in Conditional Random Fields

TL;DR

This paper extends Conditional Random Fields to jointly learn and predict interdependent structured objects while enforcing logical constraints, demonstrated on a Document Understanding task with an open source implementation.

Contribution

It introduces a general extension to CRFs for joint structured learning with logical constraints, along with an open source implementation and evaluation on a public dataset.

Findings

Improved structured prediction accuracy with logical constraints

Effective joint learning of interdependent objects

Open source tool for constrained CRF modeling

Abstract

This paper is concerned with structured machine learning, in a supervised machine learning context. It discusses how to make joint structured learning on interdependent objects of different nature, as well as how to enforce logical con-straints when predicting labels. We explain how this need arose in a Document Understanding task. We then discuss a general extension to Conditional Random Field (CRF) for this purpose and present the contributed open source implementation on top of the open source PyStruct library. We evaluate its performance on a publicly available dataset.