Human-annotated rationales and explainable text classification: a survey - Herrewijnen, Elize; Nguyen, Dong; Bex, Floris; van Deemter, Kees (Frontiers in Artificial Intelligence, 2024)
Asking annotators to explain 'why' they labelled an instance yields annotator rationales: natural language explanations that provide reasons for classifications. In this work, we survey the collection and use of annotator rationales. Human-annotated rationales can improve data quality and form a valuable resource for improving machine learning models. Moreover, human-annotated rationales can inspire the construction and evaluation of model-annotated rationales, which can play an important role in explainable artificial intelligence.