Multilingual Image Corpus – Towards a Multimodal and Multilingual Dataset (2022.lrec-1)
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| Challenge: | The goal of the project Multilingual Image Corpus is to provide a large image dataset with annotated objects and object descriptions in 24 languages. |
| Approach: | They propose to provide a large image dataset with annotated objects and object descriptions in 24 languages. |
| Outcome: | The project provides a large image dataset with annotated objects and object descriptions in 24 languages. |
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Learning Translations via Images with a Massively Multilingual Image Dataset (P18-1)
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| Challenge: | Existing datasets for learning translations of words are limited to a few high-resource languages and unrealistically easy settings. |
| Approach: | They propose a large-scale multilingual corpus of images labeled with the word they represent to facilitate translation research. |
| Outcome: | The proposed method improves on an unsupervised technique that has been limited to a few languages and unrealistic settings. |
MultiSubs: A Large-scale Multimodal and Multilingual Dataset (2022.lrec-1)
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| Challenge: | a large-scale multimodal and multilingual dataset is used to facilitate research on visual grounding of words to images in their contextual usage in language. |
| Approach: | They propose a large-scale multimodal and multilingual dataset that aims to facilitate research on grounding words to images in their contextual usage in language. |
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The DReaM Corpus: A Multilingual Annotated Corpus of Grammars for the World’s Languages (2020.lrec-1)
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| Challenge: | Until recently, language descriptions were available in paper form only, with indexes as the only search aid. |
| Approach: | They propose to digitize a multilingual corpus of language descriptions and annotate it with various meta, word, and text attributes to make searching and analysis easier and more useful. |
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CAMIO: A Corpus for OCR in Multiple Languages (2022.lrec-1)
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| Challenge: | CAMIO is a corpus of 70,000 images of machine printed text for optical character recognition (OCR) it covers 35 languages across 24 unique scripts. |
| Approach: | CAMIO is a corpus of annotated multilingual images for optical character recognition . the corpus includes nearly 70,000 images of machine printed text . |
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Babel-ImageNet: Massively Multilingual Evaluation of Vision-and-Language Representations (2024.acl-long)
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| Challenge: | Vision-and-language models with separate encoders for each modality are limited in availability. |
| Approach: | They propose a multilingual benchmark that offers (partial) translations of ImageNet labels to 100 languages, built without machine translation or manual annotation. |
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Visually Grounded Reasoning across Languages and Cultures (2021.emnlp-main)
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| Challenge: | a new protocol allows for a multilingual hierarchy of concepts and images based on native speakers . the results suggest that the current models are not robust enough to handle multilingual data . |
| Approach: | They propose a protocol to construct an ImageNet-style hierarchy representative of more languages and cultures. |
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A Multi-Modal Multilingual Benchmark for Document Image Classification (2023.findings-emnlp)
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| Challenge: | Existing document image classification datasets have several limitations and we present two new datasets that overcome these limitations. |
| Approach: | They propose to use two newly curated multilingual datasets that overcome these limitations and propose to develop multilingual Document AI models. |
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A Corpus for Multilingual Document Classification in Eight Languages (L18-1)
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| Challenge: | a subset of the Reuters corpus volume 2 is used to evaluate cross-lingual document classification . current best practice is to evaluate document classification on resources in one language and transfer it to another without additional resources. |
| Approach: | They propose to use a subset of the Reuters corpus to evaluate cross-lingual document classification . they propose to add Italian, Russian, Japanese and Chinese to the subset . |
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Systematic Analysis of Image Schemas in Natural Language through Explainable Multilingual Neural Language Processing (2022.coling-1)
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| Challenge: | Existing methods for automatic detection of image schemas in natural language rely on specific assumptions about word classes as indicators of spatio-temporal events. |
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MIT-10M: A Large Scale Parallel Corpus of Multilingual Image Translation (2025.coling-main)
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| Challenge: | Existing datasets suffer from limitations in scale, diversity, and quality, hindering the development and evaluation of IT models. |
| Approach: | They propose a large-scale parallel corpus of multilingual image translation with over 10M image-text pairs derived from real-world data. |
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