37 citations · 76 across the 8 of their papers we have counts for
9 papers
Spanish Legalese Language Model and Corpora
Asier Gutiérrez-Fandiño, Jordi Armengol-Estapé, Aitor Gonzalez-Agirre +1
There are many Language Models for the English language according to its worldwide relevance. However, for the Spanish language, even if it is a widely spoken language, there are v…
Biomedical and Clinical Language Models for Spanish: On the Benefits of Domain-Specific Pretraining in a Mid-Resource Scenario
Casimiro Pio Carrino, Jordi Armengol-Estapé, Asier Gutiérrez-Fandiño +4
This work presents biomedical and clinical language models for Spanish by experimenting with different pretraining choices, such as masking at word and subword level, varying the v…
Spanish Biomedical Crawled Corpus: A Large, Diverse Dataset for Spanish Biomedical Language Models
Casimiro Pio Carrino, Jordi Armengol-Estapé, Ona de Gibert Bonet +4
We introduce CoWeSe (the Corpus Web Salud Español), the largest Spanish biomedical corpus to date, consisting of 4.5GB (about 750M tokens) of clean plain text. CoWeSe is the result…
Are Multilingual Models the Best Choice for Moderately Under-resourced Languages? A Comprehensive Assessment for Catalan
Jordi Armengol-Estapé, Casimiro Pio Carrino, Carlos Rodriguez-Penagos +5
Multilingual language models have been a crucial breakthrough as they considerably reduce the need of data for under-resourced languages. Nevertheless, the superiority of language-…
Overview of BioASQ 2020: The eighth BioASQ challenge on Large-Scale Biomedical Semantic Indexing and Question Answering
Anastasios Nentidis, Anastasia Krithara, Konstantinos Bougiatiotis +4
In this paper, we present an overview of the eighth edition of the BioASQ challenge, which ran as a lab in the Conference and Labs of the Evaluation Forum (CLEF) 2020. BioASQ is a…
Persistent Homology Captures the Generalization of Neural Networks Without A Validation Set
Asier Gutiérrez-Fandiño, David Pérez-Fernández, Jordi Armengol-Estapé +1
The training of neural networks is usually monitored with a validation (holdout) set to estimate the generalization of the model. This is done instead of measuring intrinsic proper…