Addressing Class Imbalance for Improved Recognition of Implicit Discourse Relations

In this paper we address the problem of skewed class distribution in implicit discourse relation recognition. We examine the performance of classifiers for both binary classification predicting if a particular relation holds or not and for multi-class prediction. We review prior work to point out that the problem has been addressed differently for the binary and multi-class problems. We demonstrate that adopting a unified approach can significantly improve the performance of multi-class prediction. We also propose an approach that makes better use of the full annotations in the training set when downsampling is used. We report significant absolute improvements in performance in multi-class prediction, as well as significant improvement of binary classifiers for detecting the presence of implicit Temporal, Comparison and Contingency relations.