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Human-annotated rationales and explainable text classification: a survey
Elize Herrewijnen1,2, Dong Nguyen1, Floris Bex1,3
1Department of Information & Computing Sciences, Utrecht University, Utrecht, Netherlands.
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 artificial intelligence.
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