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The recently proposed SNLI-VE corpus for recognising visual-textualentailment is a large, real-world dataset for fine-grained multimodalreasoning. However, the automatic way in which SNLI-VE has been assembled (viacombining parts of two related datasets) gives rise to a large number of errorsin the labels of this corpus. In this paper, we first present a data collectioneffort to correct the class with the highest error rate in SNLI-VE. Secondly,we re-evaluate an existing model on the corrected corpus, which we callSNLI-VE-2.0, and provide a quantitative comparison with its performance on thenon-corrected corpus. Thirdly, we introduce e-SNLI-VE, which appendshuman-written natural language explanations to SNLI-VE-2.0. Finally, we trainmodels that learn from these explanations at training time, and output suchexplanations at testing time.

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