  Training a model with access to human explanations can improve data
efficiency and model performance on in- and out-of-domain data. Adding to these
empirical findings, similarity with the process of human learning makes
learning from explanations a promising way to establish a fruitful
human-machine interaction. Several methods have been proposed for improving
natural language processing (NLP) models with human explanations, that rely on
different explanation types and mechanism for integrating these explanations
into the learning process. These methods are rarely compared with each other,
making it hard for practitioners to choose the best combination of explanation
type and integration mechanism for a specific use-case. In this paper, we give
an overview of different methods for learning from human explanations, and
discuss different factors that can inform the decision of which method to
choose for a specific use-case.
