1 code implementation • EAMT 2020 • Gema Ramírez-Sánchez, Jaume Zaragoza-Bernabeu, Marta Bañón, Sergio Ortiz Rojas
This paper shows the utility of two open-source tools designed for parallel data cleaning: Bifixer and Bicleaner.
1 code implementation • LREC 2022 • Jaume Zaragoza-Bernabeu, Gema Ramírez-Sánchez, Marta Bañón, Sergio Ortiz Rojas
This paper describes the experiments carried out during the development of the latest version of Bicleaner, named Bicleaner AI, a tool that aims at detecting noisy sentences in parallel corpora.
no code implementations • HumEval (ACL) 2022 • Gema Ramírez-Sánchez, Marta Bañón, Jaume Zaragoza-Bernabeu, Sergio Ortiz Rojas
Quality assessment has been an ongoing activity of the series of ParaCrawl efforts to crawl massive amounts of parallel data from multilingual websites for 29 languages.
2 code implementations • ACL 2020 • Marta Ba{\~n}{\'o}n, Pin-zhen Chen, Barry Haddow, Kenneth Heafield, Hieu Hoang, Miquel Espl{\`a}-Gomis, Mikel L. Forcada, Amir Kamran, Faheem Kirefu, Philipp Koehn, Sergio Ortiz Rojas, Leopoldo Pla Sempere, Gema Ram{\'\i}rez-S{\'a}nchez, Elsa Sarr{\'\i}as, Marek Strelec, Brian Thompson, William Waites, Dion Wiggins, Jaume Zaragoza
We report on methods to create the largest publicly available parallel corpora by crawling the web, using open source software.
no code implementations • LREC 2016 • Nikola Ljube{\v{s}}i{\'c}, Miquel Espl{\`a}-Gomis, Antonio Toral, Sergio Ortiz Rojas, Filip Klubi{\v{c}}ka
This paper presents an approach for building large monolingual corpora and, at the same time, extracting parallel data by crawling the top-level domain of a given language of interest.
no code implementations • EAMT 2016 • Antonio Toral, Tommi A. Pirinen, Andy Way, Gema Ram{\'\i}rez-S{\'a}nchez, Sergio Ortiz Rojas, Raphael Rubino, Miquel Espl{\`a}, Mikel L. Forcada, Vassilis Papavassiliou, Prokopis Prokopidis, Nikola Ljube{\v{s}}i{\'c}