AUTHOR=Herrewijnen Elize , Nguyen Dong , Bex Floris , van Deemter Kees 

TITLE=Human-annotated rationales and explainable text classification: a survey

JOURNAL=Frontiers in Artificial Intelligence

VOLUME=7

YEAR=2024

URL=https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2024.1260952

DOI=10.3389/frai.2024.1260952

ISSN=2624-8212

ABSTRACT=<p>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.</p>