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.

本文提到了一个新的 SNLI-VE corpus 数据集，用于识别视觉 - 文本蕴含，介绍了一个数据收集的过程去修正该数据集的标签错误，进一步评估了更正在的数据集 SNLI-VE-2.0，并且提供了开源的人写的自然语言解释用于训练模型，最后测试它们的表现。