Challenge: CATalog 1.0 is the largest text corpus in Catalan to date . CURATE is a pipeline that can be parallelizable to run in high performance clusters .
Approach: They propose a data pipeline that uses binary filters to filter documents based on text quality . they optimised the pipeline to run in high performance clusters .
Outcome: The proposed pipeline is optimized for high performance cluster environments and runs in high performance.

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Does Corpus Quality Really Matter for Low-Resource Languages? (2022.emnlp-main)

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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.
Are Multilingual Models the Best Choice for Moderately Under-resourced Languages? A Comprehensive Assessment for Catalan (2021.findings-acl)

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Challenge: Multilingual language models have been a crucial breakthrough for under-resourced languages . however, the superiority of language-specific models has already been proven for underresourced ones .
Approach: They propose to build a monolingual monolingual model that is comparable to state-of-the-art large multilingual models.
Outcome: The proposed model consistently outperforms state-of-the-art models across tasks and settings.
The ACQDIV Corpus Database and Aggregation Pipeline (2020.lrec-1)

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Challenge: ACQDIV corpus database and aggregation pipeline aims to identify universal cognitive processes that allow children to acquire any language.
Approach: They present the ACQDIV corpus database and aggregation pipeline . the tool aims to identify universal cognitive processes that allow children to acquire any language .
Outcome: The ACQDIV corpus database and aggregation pipeline is a tool developed by the European Research Council . the database represents 15 corpora from 14 typologically maximally diverse languages .
CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data (2020.lrec-1)

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Challenge: Pre-training text representations have led to significant improvements in many areas of natural language processing.
Approach: They propose a pipeline to extract monolingual datasets from Common Crawl . pipeline follows data processing introduced in fastText that deduplicates documents .
Outcome: The proposed pipeline performs standard document deduplication and language identification similar to the pipeline introduced in fastText and a filtering step to select documents close to high quality corpora like Wikipedia.
Text Filtering Classifiers for Medium-Resource Languages (2024.lrec-main)

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Challenge: linguistic and NLP researchers use web-crawled corpora to filter low-quality texts . early Transformer-based language models were typically pre-trained on curated corporum .
Approach: They compare the effectiveness of various text filtering classifiers on Icelandic, Estonian and Basque texts . they use a perplexity-based classifier and a self-supervised classifier trained on TQ-IS to discern between documents from curated and web-crawled corpora.
Outcome: The proposed classifiers achieve F1 scores of 94.48%, 99.01% and 93.40% on the Icelandic, Estonian and Basque datasets.
Judging Quality Across Languages: A Multilingual Approach to Pretraining Data Filtering with Language Models (2025.emnlp-main)

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Challenge: Existing open-source multilingual datasets rely on heuristic filtering methods restricting both their cross-lingual transferability and scalability.
Approach: They propose a systematic approach that curates diverse and high-quality multilingual data at scale while significantly reducing computational demands.
Outcome: Evaluated empirically across 35 languages, the proposed approach outperforms current heuristic filtering methods like Fineweb2 and improves model training quality and retention rates.
Data Collection Pipeline for Low-Resource Languages: A Case Study on Constructing a Tetun Text Corpus (2024.lrec-main)

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Challenge: Labadain Crawler is a data collection pipeline designed to automate and optimize the process of constructing textual corpora from the web, with a specific target to low-resource languages.
Approach: They propose a data collection pipeline built on top of Nutch, an open-source web crawler and data extraction framework, and a tokenizer and identifier for Tetun.
Outcome: The proposed pipeline is based on Nutch, an open-source web crawler and data extraction framework, and is tested with Tetun, one of Timor-Leste’s official languages.
A Lightweight Modeling Middleware for Corpus Processing (L18-1)

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Challenge: Present-day empirical research in computational or theoretical linguistics has richly annotated and diverse corpus resources.
Approach: They propose a framework for modeling arbitrary multi-modal corpus resources in a unified form for processing tools.
Outcome: The proposed framework allows researchers to explore and query more diverse corpus resources and artifacts through a single interactive interface.
ParCourE: A Parallel Corpus Explorer for a Massively Multilingual Corpus (2021.acl-demo)

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Challenge: 7000 languages worldwide are spoken, but most research is focused on English . multilinguality is essential for multilingual research, and is a key component of the process.
Approach: They propose a wordaligned parallel corpus that can be browsed using an online tool . they use the word alignment tools SimAlign and BabelNet to find the alignments .
Outcome: The proposed tool can be set up for any parallel corpus and explores its quality and properties.
Quality Does Matter: A Detailed Look at the Quality and Utility of Web-Mined Parallel Corpora (2024.eacl-long)

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Challenge: Existing web-mined corpora for low-resource languages have serious quality issues, especially for lowresource language pairs.
Approach: They ranked each corpus according to a similarity measure and evaluated different portions of this ranked corpus.
Outcome: The results show that the quality of web-mined corpora for low-resource languages is significantly different from human-curated corporats.

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