Teach Me to Explain: A Review of Datasets for Explainable NLP

• URL: http://arxiv.org/abs/2102.12060v1
• Date: Wed, 24 Feb 2021 04:25:01 GMT
• Title: Teach Me to Explain: A Review of Datasets for Explainable NLP
• Authors: Sarah Wiegreffe and Ana Marasovi\'c
• Abstract summary: Explainable NLP (ExNLP) has increasingly focused on collecting human-annotated explanations. These explanations are used downstream in three ways: as data augmentation to improve performance on a predictive task, as a loss signal to train models to produce explanations for their predictions, and as a means to evaluate the quality of model-generated explanations. In this review, we identify three predominant classes of explanations (highlights, free-text, and structured), organize the literature on annotating each type, point to what has been learned to date, and give recommendations for collecting ExNLP datasets in the future.
• Score: 6.256505195819595
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Explainable NLP (ExNLP) has increasingly focused on collecting human-annotated explanations. These explanations are used downstream in three ways: as data augmentation to improve performance on a predictive task, as a loss signal to train models to produce explanations for their predictions, and as a means to evaluate the quality of model-generated explanations. In this review, we identify three predominant classes of explanations (highlights, free-text, and structured), organize the literature on annotating each type, point to what has been learned to date, and give recommendations for collecting ExNLP datasets in the future.

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