e-SNLI-VE-2.0: Corrected Visual-Textual Entailment with Natural Language Explanations

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Measuring Association Between Labels and Free-Text Rationales (2020, background): “This is the most common approach to free-text rationalization (Hendricks et al., 2016; Kim et al., 2018; Camburu et al., 2018; Ehsan et al., 2018; Liu et al., 2019; Wu and Mooney, 2019; Narang et al., 2020; Do et al., 2020), but little is understood about its faithfulness.”
Probing Inter-modality: Visual Parsing with Self-Attention for Vision-Language Pre-training (2021, methodology): “Figure 4: Inter-Modality Flow (IMF) within each layer of the multi-modal Transformer F i,i inter, i ∈ [1, 12] calculated on ViLT [25], SOHO [18], UNITER [9] and our model.”
Teach Me to Explain: A Review of Datasets for Explainable NLP (2021, methodology): “Do et al. (2020) re-annotate labels and explanations for the neutral pairs in the validation and test sets of SNLI-VE .”
Natural Language Rationales with Full-Stack Visual Reasoning: From Pixels to Semantic Frames to Commonsense Graphs (2020, methodology): “Still, we evaluate ∼2.5 more instances per each of 24 dataset-model combinations than related work (Camburu et al., 2018; Do et al., 2020; Narang et al., 2020); and each these samples to tune any of our hyperparameters.”