Learning Soft Linear Constraints with Application to Citation Field Extraction

TL;DR

This paper introduces a method for learning and applying soft linear constraints to improve citation field extraction, enabling automatic constraint generation and cost learning, resulting in significant accuracy gains.

Contribution

It extends dual decomposition to handle soft constraints, allowing automatic generation and learning of constraints for better citation segmentation.

Findings

Significant accuracy improvements on citation extraction dataset

Effective automatic generation of large constraint families

Successful learning of constraint costs through convex optimization

Abstract

Accurately segmenting a citation string into fields for authors, titles, etc. is a challenging task because the output typically obeys various global constraints. Previous work has shown that modeling soft constraints, where the model is encouraged, but not require to obey the constraints, can substantially improve segmentation performance. On the other hand, for imposing hard constraints, dual decomposition is a popular technique for efficient prediction given existing algorithms for unconstrained inference. We extend the technique to perform prediction subject to soft constraints. Moreover, with a technique for performing inference given soft constraints, it is easy to automatically generate large families of constraints and learn their costs with a simple convex optimization problem during training. This allows us to obtain substantial gains in accuracy on a new, challenging citation extraction dataset.