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Training a model with access to human explanations can improve dataefficiency and model performance on in- and out-of-domain data. Adding to theseempirical findings, similarity with the process of human learning makeslearning from explanations a promising way to establish a fruitfulhuman-machine interaction. Several methods have been proposed for improvingnatural language processing (NLP) models with human explanations, that rely ondifferent explanation types and mechanism for integrating these explanationsinto the learning process. These methods are rarely compared with each other,making it hard for practitioners to choose the best combination of explanationtype and integration mechanism for a specific use-case. In this paper, we givean overview of different methods for learning from human explanations, anddiscuss different factors that can inform the decision of which method tochoose for a specific use-case.

A survey on improving NLP models with human explanations