  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.
