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.

中文翻译：

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教我讲解：可解释NLP数据集的回顾

可解释的自然语言处理（ExNLP）越来越注重于收集人类注释的解释。这些解释以三种方式在下游使用：作为数据增强以提高预测任务的性能；作为损失信号来训练模型以为其预测提供解释；以及作为评估模型生成的解释的质量的手段。在这篇综述中，我们确定了三类主要的解释（突出显示，自由文本和结构化），整理了有关注释每种类型的文献，指出了迄今为止所学的内容，并提出了建议，以供将来收集ExNLP数据集。