mlsanityData to 7 Oct 2026

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

arXiv 2004.03744 ↗First posted 7 Apr 2020cs.CL cs.AI cs.CVcited by 30

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Abstract (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 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.

Key facts. Cited by 30 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Apr 2020.

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Citing papers per year: 2021: 4; 2022: 6; 2023: 6; 2024: 6; 2025: 3; 2026: 5.

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  1. 2022BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generationcited by 4,310
  2. 2021Align before Fuse: Vision and Language Representation Learning with Momentum Distillationcited by 1,612
  3. 2021Teach Me to Explain: A Review of Datasets for Explainable Natural Language Processingcited by 126
  4. 2022NLX-GPT: A Model for Natural Language Explanations in Vision and Vision-Language Taskscited by 60
  5. 2023Large Language Models are Visual Reasoning Coordinatorscited by 58
  6. 2024One Perturbation is Enough: On Generating Universal Adversarial Perturbations against Vision-Language Pre-training Modelscited by 39
  7. 2021On the Diversity and Limits of Human Explanationscited by 30
  8. 2024TV-TREES: Multimodal Entailment Trees for Neuro-Symbolic Video Reasoningcited by 12
  9. 2022A survey on knowledge-enhanced multimodal learningcited by 11
  10. 2023A Multi-Modal Context Reasoning Approach for Conditional Inference on Joint Textual and Visual Cluescited by 11
  11. 2022FRAME: Evaluating Rationale-Label Consistency Metrics for Free-Text Rationalescited by 10
  12. 2022GRIT-VLP: Grouped Mini-batch Sampling for Efficient Vision and Language Pre-trainingcited by 10
18 more
  1. 2023Dataset Bias Mitigation in Multiple-Choice Visual Question Answering and Beyondcited by 7
  2. 2024Finetuning CLIP to Reason about Pairwise Differencescited by 6
  3. 2026Visual Jev: Accurate and Efficient Decisions from Shared Visual Contextcited by 6
  4. 2023Understanding and Predicting Human Label Variation in Natural Language Inference through Explanationcited by 5
  5. 2023From Wrong To Right: A Recursive Approach Towards Vision-Language Explanationcited by 4
  6. 2025Putting on the Thinking Hats: A Survey on Chain of Thought Fine-tuning from the Perspective of Human Reasoning Mechanismcited by 4
  7. 2026BidirLM: From Text to Omnimodal Bidirectional Encoders by Adapting and Composing Causal LLMscited by 4
  8. 2024Cognitive Visual-Language Mapper: Advancing Multimodal Comprehension with Enhanced Visual Knowledge Alignmentcited by 3
  9. 2024Towards Efficient and Robust VQA-NLE Data Generation with Large Vision-Language Modelscited by 2
  10. 2023Black-box Attacks on Image Activity Prediction and its Natural Language Explanationscited by 1
  11. 2021Building a Video-and-Language Dataset with Human Actions for Multimodal Logical Inferencecited by 0
  12. 2022Accountable and Explainable Methods for Complex Reasoning over Textcited by 0
  13. 2024VEglue: Testing Visual Entailment Systems via Object-Aligned Joint Erasingcited by 0
  14. 2025Don't Learn, Ground: A Case for Natural Language Inference with Visual Groundingcited by 0
  15. 2025CAuSE: Decoding Multimodal Classifiers using Faithful Natural Language Explanationcited by 0
  16. 2026WeNLEX: Weakly Supervised Natural Language Explanations for Multilabel Chest X-ray Classificationcited by 0
  17. 2026Towards reliable multimodal disaster severity assessment through preference optimization and explainable vision-language reasoningcited by 0
  18. 2026Evaluating Explanation-Driven Vision-Language Reasoning via Generation Order Interventionscited by 0