Papers by Gema Ramírez-Sánchez
ParaCrawl: Web-Scale Acquisition of Parallel Corpora (2020.acl-main)
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Marta Bañón, Pinzhen Chen, Barry Haddow, Kenneth Heafield, Hieu Hoang, Miquel Esplà-Gomis, Mikel L. Forcada, Amir Kamran, Faheem Kirefu, Philipp Koehn, Sergio Ortiz Rojas, Leopoldo Pla Sempere, Gema Ramírez-Sánchez, Elsa Sarrías, Marek Strelec, Brian Thompson, William Waites, Dion Wiggins, Jaume Zaragoza
| Challenge: | We describe methods to create the largest publicly available parallel corpora by crawling the web . parallel corpus is essential for building highquality machine translation systems . |
| Approach: | They describe methods to create largest publicly available parallel corpora by crawling web sites . they empirically compare alternative methods and publish benchmark data sets . |
| Outcome: | The proposed methods improve state-of-the-art results on common benchmarks, the authors show . the pipeline has been tested on Russian, Sinhala, Nepali, Tagalog, Swahili, and Somali . |
Quality Beyond A Glance: Revealing Large Quality Differences Between Web-Crawled Parallel Corpora (2025.coling-main)
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| Challenge: | Parallel corpora play a vital role in advanced multilingual natural language processing tasks, notably in machine translation (MT). |
| Approach: | They manually and automatically evaluated four well-known publicly available parallel corpora across eleven language pairs. |
| Outcome: | The results show that the four well-known parallel corpora have a substantial amount of noisy sentence pairs, while CCMatrix and CCAligned have low quality sentences. |
A New Massive Multilingual Dataset for High-Performance Language Technologies (2024.lrec-main)
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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 . |
The EuroPat Corpus: A Parallel Corpus of European Patent Data (2022.lrec-1)
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| Challenge: | a new corpus of patent-specific parallel data is available for 6 official European languages paired with English: German, Spanish, French, Croatian, Norwegian, and Polish. |
| Approach: | They present a patent-specific corpus of parallel data for 6 official European languages paired with English: German, Spanish, French, Croatian, Norwegian, and Polish. |
| Outcome: | The filtered corpus ranges in size from 51 million sentences (Spanish-English) to 154k sentences (Croatian-English), with the unfiltered (raw) corpus being up to 2 times larger. |
An Expanded Massive Multilingual Dataset for High-Performance Language Technologies (HPLT) (2025.acl-long)
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Laurie Burchell, Ona De Gibert Bonet, Nikolay Arefyev, Mikko Aulamo, Marta Bañón, Pinzhen Chen, Mariia Fedorova, Liane Guillou, Barry Haddow, Jan Hajič, Jindřich Helcl, Erik Henriksson, Mateusz Klimaszewski, Ville Komulainen, Andrey Kutuzov, Joona Kytöniemi, Veronika Laippala, Petter Mæhlum, Bhavitvya Malik, Farrokh Mehryary, Vladislav Mikhailov, Nikita Moghe, Amanda Myntti, Dayyán O’Brien, Stephan Oepen, Proyag Pal, Jousia Piha, Sampo Pyysalo, Gema Ramírez-Sánchez, David Samuel, Pavel Stepachev, Jörg Tiedemann, Dušan Variš, Tereza Vojtěchová, Jaume Zaragoza-Bernabeu
| Challenge: | a large number of textual data is needed to train state-of-the-art large language models. |
| Approach: | They propose a collection of monolingual and parallel corpora from the Internet Archive . they document the entire data pipeline and release the code to reproduce it . |
| Outcome: | The proposed collection of monolingual and parallel corpora is based on the HPLT v2 dataset . it includes 8T tokens covering 193 languages and 380M sentence pairs covering 51 languages . |
Bicleaner AI: Bicleaner Goes Neural (2022.lrec-1)
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| Challenge: | a new version of Bicleaner detects noisy sentences in parallel corpora . the tool is based on pre-trained transformer-based language models fine-tuned on a binary classification task. |
| Approach: | They propose to use Bicleaner AI to detect noisy sentences in parallel corpora . they use pre-trained transformer-based language models fine-tuned on a binary classification task . |
| Outcome: | The proposed tool improves translation quality and reduces manual cleaning steps. |
Do Language Models Care about Text Quality? Evaluating Web-Crawled Corpora across 11 Languages (2024.lrec-main)
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Rik van Noord, Taja Kuzman, Peter Rupnik, Nikola Ljubešić, Miquel Esplà-Gomis, Gema Ramírez-Sánchez, Antonio Toral
| Challenge: | Large, curated, web-crawled corpora play a vital role in training language models . however, relatively little attention has been given to the quality of these corporata . |
| Approach: | They compare four of the currently most relevant large, web-crawled corpora across eleven lower-resourced European languages to evaluate their quality. |
| Outcome: | The CC100 corpus achieves the highest scores on the tests in 11 lower-resourced European languages. |
FastSpell: The LangId Magic Spell (2024.lrec-main)
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| Challenge: | Language identification is a crucial component in the automated production of language resources. |
| Approach: | They propose a language identifier that combines fastText and Hunspell to give a second opinion before deciding which language to assign to a text. |
| Outcome: | The proposed language identifier is based on a pre-trained language identifier and a spell checker. |