Human-annotated rationales and explainable text classification: a survey
Elize Herrewijnen,
Dong Nguyen,
Floris Bex
et al.
Abstract:Asking annotators to explain “why” they labeled 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 ar… Show more
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