Transformation-Based Learning in the Fast Lane

TL;DR

This paper introduces a new method to significantly reduce training time for transformation-based learning in NLP tasks without compromising accuracy, making it more practical for large datasets.

Contribution

The paper presents a novel approach that accelerates transformation-based learning training while maintaining performance, outperforming standard methods and ICA system in speed.

Findings

Training time is substantially reduced.

Performance remains comparable to standard learners.

The method is effective on large corpora.

Abstract

Transformation-based learning has been successfully employed to solve many natural language processing problems. It achieves state-of-the-art performance on many natural language processing tasks and does not overtrain easily. However, it does have a serious drawback: the training time is often intorelably long, especially on the large corpora which are often used in NLP. In this paper, we present a novel and realistic method for speeding up the training time of a transformation-based learner without sacrificing performance. The paper compares and contrasts the training time needed and performance achieved by our modified learner with two other systems: a standard transformation-based learner, and the ICA system \cite{hepple00:tbl}. The results of these experiments show that our system is able to achieve a significant improvement in training time while still achieving the same performance as a standard transformation-based learner. This is a valuable contribution to systems and algorithms which utilize transformation-based learning at any part of the execution.