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e-SNLI-VE: Corrected Visual-Textual Entailment with Natural Language Explanations
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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.
Forward citations
Cited by 2 Pith papers
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What Are Research Hypotheses?
A position paper documenting inconsistent and often implicit definitions of 'hypothesis' across NLP hypothesis mining tasks and calling for standardization.
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Probing Vision-Language Understanding through the Visual Entailment Task: promises and pitfalls
Llama 3.2 Vision reaches 83.3% on e-SNLI-VE after fine-tuning, but high explanation scores persist with black images, showing VE accuracy and BERTScore are weak evidence of visual grounding.
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