Corpus ID: 215415945

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

@article{Do2020eSNLIVE20CV,
  title={e-SNLI-VE-2.0: Corrected Visual-Textual Entailment with Natural Language Explanations},
  author={Virginie Do and Oana-Maria Camburu and Zeynep Akata and Thomas Lukasiewicz},
  journal={ArXiv},
  year={2020},
  volume={abs/2004.03744}
}
  • Virginie Do, Oana-Maria Camburu, +1 author Thomas Lukasiewicz
  • Published 2020
  • Computer Science
  • ArXiv
  • 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… CONTINUE READING

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