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25th Annual Conference of the European Association for Machine Translation, EAMT 2024

作者： Ivars-Ribes, Xavi Mas, Jordi Riera, Marc Ortolà, Jaume Cànovas, David Forcada, Mikel L. Softcatalà Softcatalà Prompsit Language Engineering Spain

ISBN: (纸本)9781068690716

Among the services provided by Softcatalà, a non-profit 25-year-old grassroots organization that localizes software into Catalan and develops software to ease the generation of Catalan content, one of the most used is its machine translation (MT) service, which provides both rule-based MT and neural MT between Catalan and twelve other languages. Development occurs in a community-supported, transparent way by using free/open-source software and open language resources. This paper briefly describes the MT services at Softcatalà: the offered functionalities, the data, and the software used to provide them. © 2024 The authors. This.

关键词： Program translators

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FastSpell: the LangId Magic Spell 30

FastSpell: the LangId Magic Spell

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on language Resources and Evaluation, LREC-COLING 2024

作者： Bañón, Marta Zaragoza-Bernabeu, Jaume Ramírez-Sánchez, Gema Ortiz-Rojas, Sergio Prompsit Language Engineering S.L. Spain

ISBN: (纸本)9782493814104

language identification is a crucial component in the automated production of language resources, particularly in multilingual and big data contexts. However, commonly used language identifiers struggle to differentiate between similar or closely-related languages. This paper introduces FastSpell, a language identifier that combines fastText (a pre-trained language identifier tool) and Hunspell (a spell checker) with the aim of having a refined second-opinion before deciding which language should be assigned to a text. We provide a description of the FastSpell algorithm along with an explanation on how to use and configure it. To that end, we motivate the need of such a tool and present a benchmark including some popular language identifiers evaluated during the development of FastSpell. We show how FastSpell is useful not only to improve identification of similar languages, but also to identify new ones ignored by other tools. © 2024 ELRA language Resource Association: CC BY-NC 4.0.

关键词： Natural language processing systems

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MutNMT, an open-source NMT tool for educational purposes 1

MutNMT, an open-source NMT tool for educational purposes

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1st Workshop on Open Community-Driven Machine Translation, crowdMT 2023

作者： Sánchez, Gema Ramírez Prompsit Language Engineering Sri Lanka

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SmartBiC: Smart Harvesting of Bilingual Corpora from the Internet 25

SmartBiC: Smart Harvesting of Bilingual Corpora from the Int...

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25th Annual Conference of the European Association for Machine Translation, EAMT 2024

作者： Ramírez-Sánchez, Gema Ortiz-Rojas, Sergio Alcover, Alicia Núñez Mateiu, Tudor N. Forcada, Mikel L. Díez Orzas, Pedro L. Carrillo, Almudena Ballester Nolasco, Giuseppe Deriard Listón, Noelia Jiménez Prompsit Language Engineering Spain Linguaserve Int. de Servicios Spain

ISBN: (纸本)9781068690716

SmartBiC, an 18-month innovation project funded by the Spanish Government, aims at improving the full process of collecting, filtering and selecting in-domain parallel content to be used for machine translation and language model tuning purposes in industrial settings. Based on state-of-the-art technology in the free/open-source parallel web corpora harvester Bitextor, SmartBic develops a web-based application around it including novel components such as a language -and domain- focused crawler and a domain-specific corpora selector. SmartBic also addresses specific industrial use cases for individual components of the Bitextor pipeline, such as parallel data cleaning. Relevant improvements to the current Bitextor pipeline will be publicly released. © 2024 The authors.

关键词： Web crawler

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HPLT: High Performance language Technologies 24

HPLT: High Performance Language Technologies

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24th Annual Conference of the European Association for Machine Translation, EAMT 2023

作者： Aulamo, Mikko Bogoychev, Nikolay Ji, Shaoxiong Nail, Graeme Ramírez-Sánchez, Gema Tiedemann, Jörg van der Linde, Jelmer Zaragoza, Jaume University of Helsinki Finland University of Edinburgh United Kingdom Prompsit Language Engineering United States

ISBN: (纸本)9789520329471

We describe the High Performance language Technologies project (HPLT), a 3-year EU-funded project started in September 2022. HPLT will build a space combining petabytes of natural language data with large-scale model training. It will derive monolingual and bilingual datasets from the Internet Archive and CommonCrawl and build efficient and solid machine translation (MT) as well as large language models (LLMs). HPLT aims at providing free, sustainable and reusable datasets, models and workflows at scale using high-performance computing (HPC). © 2023 The authors. This article is licensed under a Creative Commons 4.0 licence, no derivative works, attribution, CC-BY-ND.

关键词： Large datasets

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HPLT’s First Release of Data and Models 25

HPLT’s First Release of Data and Models

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25th Annual Conference of the European Association for Machine Translation, EAMT 2024

作者： Arefyev, Nikolay Aulamo, Mikko Chen, Pinzhen de Gibert, Ona Haddow, Barry Helcl, Jindřich Malik, Bhavitvya Ramírez-Sánchez, Gema Stepachev, Pavel Tiedemann, Jörg Variš, Dušan Zaragoza, Jaume University of Oslo Norway University of Helsinki Finland University of Edinburgh United Kingdom Charles University Czech Republic Prompsit Language Engineering Spain

ISBN: (纸本)9781068690716

The High Performance language Technologies (HPLT) project is a 3-year EU-funded project that started in September 2022. It aims to deliver free, sustainable, and reusable datasets, models, and work-flows at scale using high-performance computing. We describe the first results of the project. The data release includes monolingual data in 75 languages at 5.6T tokens and parallel data in 18 language pairs at 96M pairs, derived from 1.8 petabytes of web crawls. Building upon automated and transparent pipelines, the first machine translation (MT) models as well as large language models (LLMs) have been trained and released. Multiple data processing tools and pipelines have also been made public. © 2024 The authors.

关键词： Pipeline processing systems

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FastSpell: the LangId Magic Spell

arXiv

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arXiv 2024年

作者： Bañón, Marta Zaragoza-Bernabeu, Jaume Ramírez-Sánchez, Gema Ortiz-Rojas, Sergio Prompsit Language Engineering S.L. Spain

关键词： Natural language processing systems

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An Expanded Massive Multilingual Dataset for High-Performance language Technologies

arXiv

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arXiv 2025年

作者： Burchell, Laurie de Gibert, Ona Arefyev, Nikolay Aulamo, Mikko Bañón, Marta Chen, Pinzhen Fedorova, Mariia Guillou, Liane Haddow, Barry Hajič, Jan Helcl, Jindřich Henriksson, Erik Klimaszewski, Mateusz Komulainen, Ville Kutuzov, Andrey Kytöniemi, Joona Laippala, Veronika Mæhlum, Petter Malik, Bhavitvya Mehryary, Farrokh Mikhailov, Vladislav Moghe, Nikita Myntti, Amanda O’Brien, Dayyán Oepen, Stephan Pal, Proyag Piha, Jousia Pyysalo, Sampo Ramírez-Sánchez, Gema Samuel, David Stepachev, Pavel Tiedemann, Jörg Variš, Dušan Vojtěchová, Tereza Zaragoza-Bernabeu, Jaume University of Edinburgh United Kingdom University of Helsinki Finland University of Oslo Norway Prompsit Language Engineering Spain Charles University Czech Republic University of Turku Finland

Training state-of-the-art large language models requires vast amounts of clean and diverse textual data. However, building suitable multilingual datasets remains a challenge. In this work, we present HPLT v2, a collection of high-quality multilingual monolingual and parallel corpora. The monolingual portion of the data contains 8T tokens covering 193 languages, while the parallel data contains 380M sentence pairs covering 51 languages. We document the entire data pipeline and release the code to reproduce it. We provide extensive analysis of the quality and characteristics of our data. Finally, we evaluate the performance of language models and machine translation systems trained on HPLT v2, demonstrating its value. © 2025, CC BY.

关键词： Machine translation

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Preface by the Programme Chairs

Proceedings of the 25th Annual Conference of the European As...

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Proceedings of the 25th Annual Conference of the European Association for Machine Translation, EAMT 2024 2024年 2卷 vi-ix页

作者： Bawden, Rachel Sánchez-Cartagena, Víctor M. Cadwell, Patrick Lapshinova-Koltunski, Ekaterina Cabarrão, Vera Chatzitheodorou, Konstantinos Moniz, Helena Forcada, Mikel Nurminen, Mary Kanojia, Diptesh Haddow, Barry Inria Paris France University of Alacant Spain DCU Ireland University of Hildesheim Germany Unbabel Portugal Strategic Agenda United Kingdom INESC-ID Portugal Prompsit Language Engineering Elx Spain Tampere University Finland University of Surrey United Kingdom University of Edinburgh United Kingdom

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Bicleaner at WMT 2020: Universitat d'Alacant-prompsit's submission to the parallel corpus filtering shared task 5

Bicleaner at WMT 2020: Universitat d'Alacant-Prompsit's subm...

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5th Conference on Machine Translation, WMT 2020

作者： Esplà-Gomis, Miquel Sánchez-Cartagena, Víctor M. Zaragoza-Bernabeu, Jaume Sánchez-Martínez, Felipe Dep. de Llenguatges i Sistemes Informàtics Universitat d'Alacant Sant Vicent del Raspeig E-03690 Spain Prompsit Language Engineering Av. Universitat s/n. Edifici Quorum III ElxE-03202 Spain

ISBN: (纸本)9781948087810

This paper describes the joint submission of Universitat d'Alacant and prompsit language engineering to the WMT 2020 shared task on parallel corpus filtering. Our submission, based on the free/open-source tool Bicleaner, enhances it with Extremely Randomised Trees and lexical similarity features that account for the frequency of the words in the parallel sentences to determine if two sentences are parallel. To train this classifier we used the clean corpora provided for the task and synthetic noisy parallel sentences. In addition we re-score the output of Bicleaner using character-level language models and n-gram saturation. © 2020 Association for Computational Linguistics

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