Papers with CommonCrawl
Language-agnostic BERT Sentence Embedding (2022.acl-long)
Copied to clipboard
| Challenge: | Existing methods for learning bilingual sentence embeddings are not well explored. |
| Approach: | They propose to combine best methods for learning multilingual sentence embeddings with pre-trained models to achieve 83.7% bi-text retrieval accuracy over 112 languages on Tatoeba. |
| Outcome: | The proposed model achieves 83.7% bi-text retrieval accuracy over 112 languages on Tatoeba, above the 65.5% achieved by LASER. |
A New Massive Multilingual Dataset for High-Performance Language Technologies (2024.lrec-main)
Copied to clipboard
Ona de Gibert, Graeme Nail, Nikolay Arefyev, Marta Bañón, Jelmer van der Linde, Shaoxiong Ji, Jaume Zaragoza-Bernabeu, Mikko Aulamo, Gema Ramírez-Sánchez, Andrey Kutuzov, Sampo Pyysalo, Stephan Oepen, Jörg Tiedemann
| Challenge: | a new massive multilingual dataset is available for language modeling and machine translation training. |
| Approach: | They present a massive multilingual dataset using web crawls from the Internet Archive and CommonCrawl . they use open-source software tools and high-performance computing to acquire, manage and process large corpora . |
| Outcome: | The HPLT language resources is a massive multilingual dataset . it includes monolingual and bilingual corpora extracted from CommonCrawl and the Internet Archive . the results are published online at the journal journal cense4 . |
Does Corpus Quality Really Matter for Low-Resource Languages? (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing work on multilingual pre-training has relied on automatically filtered versions of CommonCrawl. |
| Approach: | They propose to use tailored crawling to identify and scrape websites with high-quality content to improve representation learning in Basque. |
| Outcome: | The proposed corpus, called EusCrawl, has a much higher quality according to native annotators than the Basque portion of popular multilingual corpora like CC100 and mC4. |
InfiMM-WebMath-40B: Advancing Multimodal Pre-Training for Enhanced Mathematical Reasoning (2025.findings-emnlp)
Copied to clipboard
Xiaotian Han, Yiren Jian, Xuefeng Hu, Haogeng Liu, Yiqi Wang, Qihang Fan, Yuang Ai, Huaibo Huang, Ran He, Zhenheng Yang, Quanzeng You
| Challenge: | InfiMM-WebMath-40B is a dataset of interleaved image-text documents . it consists of 24 million web pages, 85 million image URLs, and 40 billion text tokens . |
| Approach: | InfiMM-WebMath-40B is a high-quality dataset of interleaved image-text documents . it contains 24 million web pages, 85 million image URLs, and 40 billion text tokens . |
| Outcome: | InfiMM-WebMath-40B is a high-quality dataset of interleaved image-text documents . it consists of 24 million web pages, 85 million image URLs, and 40 billion text tokens . |