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

• URL: http://arxiv.org/abs/2004.03744v3
• Date: Thu, 19 Aug 2021 09:26:21 GMT
• Title: e-SNLI-VE: Corrected Visual-Textual Entailment with Natural Language Explanations
• Authors: Virginie Do, Oana-Maria Camburu, Zeynep Akata and Thomas Lukasiewicz
• Abstract summary: We present a data collection effort to correct the class with the highest error rate in SNLI-VE. Thirdly, we introduce e-SNLI-VE, which appends human-written natural language explanations to SNLI-VE. We train models that learn from these explanations at training time, and output such explanations at testing time.
• Score: 87.71914254873857
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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