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

arXiv: https://arxiv.org/abs/2004.03744 · HTML: https://mlsanity.com/paper/2004.03744/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Apr 2020.

## Built on by (most cited first, 30 of 31)

- [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://mlsanity.com/paper/2201.12086/) (2022)
- [Align before Fuse: Vision and Language Representation Learning with Momentum Distillation](https://mlsanity.com/paper/2107.07651/) (2021)
- [Teach Me to Explain: A Review of Datasets for Explainable Natural Language Processing](https://mlsanity.com/paper/2102.12060/) (2021)
- [NLX-GPT: A Model for Natural Language Explanations in Vision and Vision-Language Tasks](https://mlsanity.com/paper/2203.05081/) (2022)
- [Large Language Models are Visual Reasoning Coordinators](https://mlsanity.com/paper/2310.15166/) (2023)
- [One Perturbation is Enough: On Generating Universal Adversarial Perturbations against Vision-Language Pre-training Models](https://mlsanity.com/paper/2406.05491/) (2024)
- [On the Diversity and Limits of Human Explanations](https://mlsanity.com/paper/2106.11988/) (2021)
- [TV-TREES: Multimodal Entailment Trees for Neuro-Symbolic Video Reasoning](https://mlsanity.com/paper/2402.19467/) (2024)
- [A survey on knowledge-enhanced multimodal learning](https://mlsanity.com/paper/2211.12328/) (2022)
- [A Multi-Modal Context Reasoning Approach for Conditional Inference on Joint Textual and Visual Clues](https://mlsanity.com/paper/2305.04530/) (2023)
- [FRAME: Evaluating Rationale-Label Consistency Metrics for Free-Text Rationales](https://mlsanity.com/paper/2207.00779/) (2022)
- [GRIT-VLP: Grouped Mini-batch Sampling for Efficient Vision and Language Pre-training](https://mlsanity.com/paper/2208.04060/) (2022)
- [Visual Jev: Accurate and Efficient Decisions from Shared Visual Context](https://mlsanity.com/paper/2609.25845/) (2026)
- [Dataset Bias Mitigation in Multiple-Choice Visual Question Answering and Beyond](https://mlsanity.com/paper/2310.14670/) (2023)
- [Finetuning CLIP to Reason about Pairwise Differences](https://mlsanity.com/paper/2409.09721/) (2024)
- [Understanding and Predicting Human Label Variation in Natural Language Inference through Explanation](https://mlsanity.com/paper/2304.12443/) (2023)
- [BidirLM: From Text to Omnimodal Bidirectional Encoders by Adapting and Composing Causal LLMs](https://mlsanity.com/paper/2604.02045/) (2026)
- [From Wrong To Right: A Recursive Approach Towards Vision-Language Explanation](https://mlsanity.com/paper/2311.12391/) (2023)
- [Putting on the Thinking Hats: A Survey on Chain of Thought Fine-tuning from the Perspective of Human Reasoning Mechanism](https://mlsanity.com/paper/2510.13170/) (2025)
- [Cognitive Visual-Language Mapper: Advancing Multimodal Comprehension with Enhanced Visual Knowledge Alignment](https://mlsanity.com/paper/2402.13561/) (2024)
- [Towards Efficient and Robust VQA-NLE Data Generation with Large Vision-Language Models](https://mlsanity.com/paper/2409.14785/) (2024)
- [Black-box Attacks on Image Activity Prediction and its Natural Language Explanations](https://mlsanity.com/paper/2310.00503/) (2023)
- [CAuSE: Decoding Multimodal Classifiers using Faithful Natural Language Explanation](https://mlsanity.com/paper/2512.06814/) (2025)
- [Building a Video-and-Language Dataset with Human Actions for Multimodal Logical Inference](https://mlsanity.com/paper/2106.14137/) (2021)
- [Accountable and Explainable Methods for Complex Reasoning over Text](https://mlsanity.com/paper/2211.04946/) (2022)
- [VEglue: Testing Visual Entailment Systems via Object-Aligned Joint Erasing](https://mlsanity.com/paper/2403.02581/) (2024)
- [Don't Learn, Ground: A Case for Natural Language Inference with Visual Grounding](https://mlsanity.com/paper/2511.17358/) (2025)
- [WeNLEX: Weakly Supervised Natural Language Explanations for Multilabel Chest X-ray Classification](https://mlsanity.com/paper/2603.18752/) (2026)
- [Towards reliable multimodal disaster severity assessment through preference optimization and explainable vision-language reasoning](https://mlsanity.com/paper/2609.00879/) (2026)
- [Evaluating Explanation-Driven Vision-Language Reasoning via Generation Order Interventions](https://mlsanity.com/paper/2609.29496/) (2026)

## 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.