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.
@article{arxiv.2004.03744,
title = {e-SNLI-VE: Corrected Visual-Textual Entailment with Natural Language Explanations},
author = {Virginie Do and Oana-Maria Camburu and Zeynep Akata and Thomas Lukasiewicz},
journal= {arXiv preprint arXiv:2004.03744},
year = {2021}
}