WIT : Wikipedia-based Image Text Dataset

Wikipedia-based Image Text (WIT) Dataset is a large multimodal multilingual dataset. WIT is composed of a curated set of 37.6 million entity rich image-text examples with 11.5 million unique images across 108 Wikipedia languages. Its size enables WIT to be used as a pretraining dataset for multimodal machine learning models.

Key Advantages

A few unique advantages of WIT:

• The largest multimodal dataset (publicly available at the time of this writing) by the number of image-text examples.
• A massively multilingual dataset (first of its kind) with coverage for 108 languages.
• First image-text dataset with page level metadata and contextual information
• A collection of diverse set of concepts and real world entities.
• Brings forth challenging real-world test sets.

You can learn more about WIT Dataset from our arXiv paper.

Latest Updates

2021 April: Happy to share the good news that our paper got accepted at SIGIR Conference. From ACM site, you can find our paper, slides and presentation.

2021 September: WIT Image-Text Competition is live on Kaggle. Our collaborators from Wikimedia Research blogged about this and they have made available the raw pixels and resnet50 embeddings for the images in this set. Here is our Google AI blog post.

2022 April: We are happy to share that the WIT paper and dataset was awarded the WikiMedia Foundation's Research Award of the Year (tweet 1, tweet 2). We are deeply honored and thank you for the recognition.

2022 May: We have released the WIT validation set and test set. Please see the data page for download links.

2022 Oct: Authoring Tools for Multimedia Content proposal accepted at TREC 2023

2023 Apr: AToMiC accepted at SIGIR 2023.

2023 Apr: WikiWeb2M Dataset released.

2023 May: Accepted submissions at WikiWorkshop 2023.

• WikiWeb2M: A Page-Level Multimodal Wikipedia Dataset (pdf, arXiv)
• Building Authoring Tools for Multimedia Content with Human-in-the-loop Relevance Annotations (pdf)
• Characterizing Image Accessibility on Wikipedia across Languages (pdf)

WIT Example

Wikipedia Page

For example, let's take the Wikipedia page for Half Dome, Yosemite in CA.

From the Wikipedia page for Half Dome : Photo by DAVID ILIFF. License: CC BY-SA 3.0

Wikipedia Page with Annotations of what we can extract

From this page, we highlight the various key pieces of data that we can extract - images, their respective text snippets and some contextual metadata.

By extracting and filtering these carefully, we get a clean, high quality image-text example that can be used in multimodal modeling.

Motivation

Multimodal visio-linguistic models rely on a rich dataset to help them learn to model the relationship between images and texts. Having large image-text datasets can significantly improve performance, as shown by recent works. Furthermore the lack of language coverage in existing datasets (which are mostly only in English) also impedes research in the multilingual multimodal space – we consider this a lost opportunity given the potential shown in leveraging images (as a language-agnostic medium) to help improve our multilingual textual understanding.

To address these challenges and advance research on multilingual, multimodal learning we created the Wikipedia-based Image Text (WIT) Dataset. WIT is created by extracting multiple different texts associated with an image (e.g., as shown in the above image) from Wikipedia articles and Wikimedia image links. This was accompanied by rigorous filtering to only retain high quality image-text sets.

The resulting dataset contains over 37.6 million image-text sets – making WIT the largest multimodal dataset (publicly available at the time of this writing) with unparalleled multilingual coverage – with 12K+ examples in each of 108 languages (53 languages have 100K+ image-text pairs).

WIT: Dataset Numbers

Type	Train	Val	Test	Total / Unique
Rows / Tuples	37.13M	261.8K	210.7K	37.6M
Unique Images	11.4M	58K	57K	11.5M
Ref. Text	16.9M	150K	104K	17.2M / 16.7M
Attr. Text	34.8M	193K	200K	35.2M / 10.9M
Alt Text	5.3M	29K	29K	5.4M / 5.3M
Context Texts	-	-	-	119.8M

WIT: Image-Text Stats by Language

Image-Text	# Lang	Uniq. Images	# Lang
total > 1M	9	images > 1M	6
total > 500K	10	images > 500K	12
total > 100K	36	images > 100K	35
total > 50K	15	images > 50K	17
total > 14K	38	images > 13K	38

Get WIT

We believe that such a powerful diverse dataset will aid researchers in building better multimodal multilingual models and in identifying better learning and representation techniques leading to improvement of Machine Learning models in real-world tasks over visio-linguistic data.

WIT Dataset is now available for download. Please check the data page.

Citing WIT

If you use the WIT dataset, you can cite our work as follows.

@inproceedings{10.1145/3404835.3463257,
author = {Srinivasan, Krishna and Raman, Karthik and Chen, Jiecao and Bendersky, Michael and Najork, Marc},
title = {WIT: Wikipedia-Based Image Text Dataset for Multimodal Multilingual Machine Learning},
year = {2021},
isbn = {9781450380379},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3404835.3463257},
doi = {10.1145/3404835.3463257},
booktitle = {Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {2443–2449},
numpages = {7},
keywords = {dataset, multimodal, machine learning, wikipedia, multilingual, image-text retrieval, neural networks},
location = {Virtual Event, Canada},
series = {SIGIR '21}
}

License

This data is available under the Creative Commons Attribution-ShareAlike 3.0 Unported license.

Projects using WIT

For information regarding MURAL (Multimodal, Multitask Retrieval Across Languages) paper accepted at EMNLP 2021.

Contact

For any questions, please contact wit-dataset@google.com. To any questions to the first author, Krishna, please reach via their personal page krishna2.com for contact informaiton.

If WIT dataset is useful to you, please do write to us about it. Be it a blog post, a research project or a paper, we are delighted to learn about it.