Awesome

SNLI-VE: Visual Entailment Dataset

Ning Xie, Farley Lai, Derek Doran, Asim Kadav

SNLI-VE is the dataset proposed for the Visual Entailment (VE) task investigated in Visual Entailment Task for Visually-Grounded Language Learning accpeted to NeurIPS 2018 ViGIL workshop). Refer to our full paper for detailed analysis and evaluations.

Updates

12/10/2021:

• The Flickr images download is updated and now hosted by AlleNLP
• The Flickr features download link is updated but the archive may require newer unzip to decompress on Linux

NOTE:

• The data remains hosted by external parties and subject to change

Leaderboard

Checkout the leaderboard from paperswith code

NOTE

e-SNLI-VE-2.0 relabels the dev as well as test splits of the neutral class and evalutes the resulting performance in order of the original, val-correction and val/test correction configurations.

Overview

SNLI-VE is built on top of SNLI and Flickr30K. The problem that VE is trying to solve is to reason about the relationship between an image premise P_image and a text hypothesis H_text.

Specifically, given an image as premise, and a natural language sentence as hypothesis, three labels (entailment, neutral and contradiction) are assigned based on the relationship conveyed by the (P_image, H_text)

• entailment holds if there is enough evidence in P_image to conclude that H_text is true.
• contradiction holds if there is enough evidence in P_image to conclude that H_text is false.
• Otherwise, the relationship is neutral, implying the evidence in P_image is insufficient to draw a conclusion about H_text.

Examples from SNLI-VE

SNLI-VE Statistics

Below is some highlighted dataset statistic, details can be found in our paper.

Distribution by Split

The data details of train, dev and test split is shown below. The instances of three labels (entailment, neutral and contradiction) are evenly distributed for each split.

Dataset Comparision

Below is a dataset comparison among SNLI-VE, VQA-v2.0 and CLEVR.

Question Length Distribution

The question here for SNLI-VE dataset is the hypothesis. As shown in the figure, the question length of SNLI-VE dataset is distributed with a quite long tail.

Caveats

To check the quality of SNLI-VE dataset, we randomly sampled 217 pairs from all three splits (565286 pairs in total). Among all sampled pairs, 20 (about 9.2%) examples are incorrectly labeled, among which the majority is in the neutral class. This is consistent to the analysis reported by GTE in its Table 2.

It is worth noting that the original SNLI dataset is not perfectly labeled, with 8.8% of the sampled data not assigned a gold label, implying the disagreement within human labelers. SNLI-VE is no exception but we believe it is a common scenario in other large scale datasets. However, if the dataset quality is a major concern to you, we suggest dropping the neutral classs and only use entailment and contradiction examples.

SNLI-VE Creation

snli_ve_generator.py generates the SNLI-VE dataset in train, dev and test splits with disjoint image sets. Each entry contains a Flickr30kID field to associate with the original Flickr30K image id.

snli_ve_parser.py parses entires in SNLI-VE for applications and is free to revise.

Follow the instructions below to set up the environment and generate SNLI-VE:

1. Set the conda environment and dependencies

   conda create -n vet37 python=3.7
   conda activate vet37
   conda install jsonlines
   # conda install -c NECLA-ML ml
   

2. Clone the repo

   git clone https://github.com/necla-ml/SNLI-VE.git
   

3. Generate SNLI-VE in data/

   cd SNLI-VE
   python -m vet.tools.snli_ve_generator.py
   

4. Download dependent datasets: Flickr30K, Entities, SNLI, and RoI features

   cd data
   ./download # y to all if necessary
   

SNLI-VE Extensions

Flickr30k Entities dataset is an extension to Flickr30k, which contains grounded RoI and entity annotations.

It is easy to extend our SNLI-VE dataset with Flickr30k Entities if fine-grained annotations are required in your experiments.

Bibtex

The first is our full paper while the second is the ViGiL workshop version.

@article{xie2019visual,
  title={Visual Entailment: A Novel Task for Fine-grained Image Understanding},
  author={Xie, Ning and Lai, Farley and Doran, Derek and Kadav, Asim},
  journal={arXiv preprint arXiv:1901.06706},
  year={2019}
}

@article{xie2018visual,
  title={Visual Entailment Task for Visually-Grounded Language Learning},
  author={Xie, Ning and Lai, Farley and Doran, Derek and Kadav, Asim},
  journal={arXiv preprint arXiv:1811.10582},
  year={2018}
}  

Thank you for your interest in our dataset!
Please contact us for any questions, comments, or suggestions!