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Human-annotated rationales and explainable text classification: a survey
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DOI：10.3389/frai.2024.1260952.png)
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Abstract
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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.
Keywords:
annotator rationales
natural language explanations
explainable artificial intelligence
data collection
machine learning
rationale agreement
text classification
human-annotated rationales
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