  Learning from rationales seeks to augment model prediction accuracy using
human-annotated rationales (i.e. subsets of input tokens) that justify their
chosen labels, often in the form of intermediate or multitask supervision.
While intuitive, this idea has proven elusive in practice. We make two
observations about human rationales via empirical analyses: 1) maximizing
rationale supervision accuracy is not necessarily the optimal objective for
improving model accuracy; 2) human rationales vary in whether they provide
sufficient information for the model to exploit for prediction. Building on
these insights, we propose several novel loss functions and learning
strategies, and evaluate their effectiveness on three datasets with human
rationales. Our results demonstrate consistent improvements over baselines in
both label and rationale accuracy, including a 3% accuracy improvement on
MultiRC. Our work highlights the importance of understanding properties of
human explanations and exploiting them accordingly in model training.
