Multidimensional Transformation-Based Learning

TL;DR

This paper introduces a multidimensional transformation-based learning method that jointly trains on multiple NLP tasks, improving accuracy over sequential training and achieving state-of-the-art results in POS tagging.

Contribution

It presents a novel joint learning algorithm for multiple classification tasks within the transformation-based learning framework, enhancing performance in NLP applications.

Findings

Joint training improves task accuracy over sequential methods.

Achieved 96.63% POS tagging accuracy, a state-of-the-art result.

Demonstrated effectiveness on English and Chinese NLP tasks.

Abstract

This paper presents a novel method that allows a machine learning algorithm following the transformation-based learning paradigm \cite{brill95:tagging} to be applied to multiple classification tasks by training jointly and simultaneously on all fields. The motivation for constructing such a system stems from the observation that many tasks in natural language processing are naturally composed of multiple subtasks which need to be resolved simultaneously; also tasks usually learned in isolation can possibly benefit from being learned in a joint framework, as the signals for the extra tasks usually constitute inductive bias. The proposed algorithm is evaluated in two experiments: in one, the system is used to jointly predict the part-of-speech and text chunks/baseNP chunks of an English corpus; and in the second it is used to learn the joint prediction of word segment boundaries and part-of-speech tagging for Chinese. The results show that the simultaneous learning of multiple tasks does achieve an improvement in each task upon training the same tasks sequentially. The part-of-speech tagging result of 96.63% is state-of-the-art for individual systems on the particular train/test split.