  The recently proposed SNLI-VE corpus for recognising visual-textual
entailment is a large, real-world dataset for fine-grained multimodal
reasoning. However, the automatic way in which SNLI-VE has been assembled (via
combining parts of two related datasets) gives rise to a large number of errors
in the labels of this corpus. In this paper, we first present a data collection
effort 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 call
SNLI-VE-2.0, and provide a quantitative comparison with its performance on the
non-corrected corpus. Thirdly, we introduce e-SNLI-VE, which appends
human-written natural language explanations to SNLI-VE-2.0. Finally, we train
models that learn from these explanations at training time, and output such
explanations at testing time.
