Task Refinement Learning for Improved Accuracy and Stability ofUnsupervised Domain Adaptation
Abstract
Pivot Based Language Modeling (PBLM)
(Ziser and Reichart, 2018a), combining
LSTMs with pivot-based methods, has yielded
significant progress in unsupervised domain
adaptation. However, this approach is still
challenged by the large pivot detection problem that should be solved, and by the inherent instability of LSTMs. In this paper we
propose a Task Refinement Learning (TRL) approach, in order to solve these problems. Our
algorithms iteratively train the PBLM model,
gradually increasing the information exposed
about each pivot. TRL-PBLM achieves stateof-the-art accuracy in six domain adaptation
setups for sentiment classification. Moreover,
it is much more stable than plain PBLM across
model configurations, making the model much
better fitted for practical use