Teach Me to Explain: A Review of Datasets for Explainable Natural Language ProcessingDownload PDF

08 Jun 2021, 00:07 (modified: 12 Jan 2022, 20:10)NeurIPS 2021 Datasets and Benchmarks Track (Round 1)Readers: Everyone
Keywords: NLP, explainability, explainable NLP, explainable AI, XAI
TL;DR: We identify datasets with 3 classes of textual explanations, organize the literature on annotating each type, identify strengths/shortcomings of existing collection methodologies, and give recommendations for collecting explanations in the future.
Abstract: Explainable Natural Language Processing (ExNLP) has increasingly focused on collecting human-annotated textual explanations. These explanations are used downstream in three ways: as data augmentation to improve performance on a predictive task, as supervision to train models to produce explanations for their predictions, and as a ground-truth to evaluate model-generated explanations. In this review, we identify 65 datasets with three predominant classes of textual explanations (highlights, free-text, and structured), organize the literature on annotating each type, identify strengths and shortcomings of existing collection methodologies, and give recommendations for collecting ExNLP datasets in the future.
Supplementary Material: zip
URL: https://exnlpdatasets.github.io/
Contribution Process Agreement: Yes
Dataset Url: https://exnlpdatasets.github.io/
Dataset Embargo: N/A
License: N/A
Author Statement: Yes
8 Replies

Loading