@inproceedings{palomar-giner-etal-2024-curated-catalog,
title = "A {CURATE}d {CAT}alog: Rethinking the Extraction of Pretraining Corpora for Mid-Resourced Languages",
author = "Palomar-Giner, Jorge and
Saiz, Jose Javier and
Espu{\~n}a, Ferran and
Mina, Mario and
Da Dalt, Severino and
Llop, Joan and
Ostendorff, Malte and
Ortiz Suarez, Pedro and
Rehm, Georg and
Gonzalez-Agirre, Aitor and
Villegas, Marta",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.31",
pages = "335--349",
abstract = "We present and describe two language resources in this paper: CATalog 1.0, the largest text corpus in Catalan to date, and CURATE (Corpus Utility for RAting TExt), a modular, parallelizable pipeline used for processing and scoring documents based on text quality that we have optimised to run in High Performance Cluster (HPC) environments. In the coming sections we describe our data preprocessing pipeline at length; traditional pipelines usually implement a set of binary filters such that a given document is either in or out. In our experience with Catalan, in lower-resource settings it is more practical to instead assign a document a soft score to allow for more flexible decision-making. We describe how the document score is calculated and highlight its interpretability by showing that it is significantly correlated with human judgements as obtained from a comparative judgement experiment. We additionally describe the different subcorpora that make up CATalog 1.0.",
}
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<abstract>We present and describe two language resources in this paper: CATalog 1.0, the largest text corpus in Catalan to date, and CURATE (Corpus Utility for RAting TExt), a modular, parallelizable pipeline used for processing and scoring documents based on text quality that we have optimised to run in High Performance Cluster (HPC) environments. In the coming sections we describe our data preprocessing pipeline at length; traditional pipelines usually implement a set of binary filters such that a given document is either in or out. In our experience with Catalan, in lower-resource settings it is more practical to instead assign a document a soft score to allow for more flexible decision-making. We describe how the document score is calculated and highlight its interpretability by showing that it is significantly correlated with human judgements as obtained from a comparative judgement experiment. We additionally describe the different subcorpora that make up CATalog 1.0.</abstract>
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%0 Conference Proceedings
%T A CURATEd CATalog: Rethinking the Extraction of Pretraining Corpora for Mid-Resourced Languages
%A Palomar-Giner, Jorge
%A Saiz, Jose Javier
%A Espuña, Ferran
%A Mina, Mario
%A Da Dalt, Severino
%A Llop, Joan
%A Ostendorff, Malte
%A Ortiz Suarez, Pedro
%A Rehm, Georg
%A Gonzalez-Agirre, Aitor
%A Villegas, Marta
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F palomar-giner-etal-2024-curated-catalog
%X We present and describe two language resources in this paper: CATalog 1.0, the largest text corpus in Catalan to date, and CURATE (Corpus Utility for RAting TExt), a modular, parallelizable pipeline used for processing and scoring documents based on text quality that we have optimised to run in High Performance Cluster (HPC) environments. In the coming sections we describe our data preprocessing pipeline at length; traditional pipelines usually implement a set of binary filters such that a given document is either in or out. In our experience with Catalan, in lower-resource settings it is more practical to instead assign a document a soft score to allow for more flexible decision-making. We describe how the document score is calculated and highlight its interpretability by showing that it is significantly correlated with human judgements as obtained from a comparative judgement experiment. We additionally describe the different subcorpora that make up CATalog 1.0.
%U https://aclanthology.org/2024.lrec-main.31
%P 335-349
Markdown (Informal)
[A CURATEd CATalog: Rethinking the Extraction of Pretraining Corpora for Mid-Resourced Languages](https://aclanthology.org/2024.lrec-main.31) (Palomar-Giner et al., LREC-COLING 2024)
ACL
- Jorge Palomar-Giner, Jose Javier Saiz, Ferran Espuña, Mario Mina, Severino Da Dalt, Joan Llop, Malte Ostendorff, Pedro Ortiz Suarez, Georg Rehm, Aitor Gonzalez-Agirre, and Marta Villegas. 2024. A CURATEd CATalog: Rethinking the Extraction of Pretraining Corpora for Mid-Resourced Languages. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 335–349, Torino, Italia. ELRA and ICCL.