Casimiro Pio Carrino


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Pretrained Biomedical Language Models for Clinical NLP in Spanish
Casimiro Pio Carrino | Joan Llop | Marc Pàmies | Asier Gutiérrez-Fandiño | Jordi Armengol-Estapé | Joaquín Silveira-Ocampo | Alfonso Valencia | Aitor Gonzalez-Agirre | Marta Villegas
Proceedings of the 21st Workshop on Biomedical Language Processing

This work presents the first large-scale biomedical Spanish language models trained from scratch, using large biomedical corpora consisting of a total of 1.1B tokens and an EHR corpus of 95M tokens. We compared them against general-domain and other domain-specific models for Spanish on three clinical NER tasks. As main results, our models are superior across the NER tasks, rendering them more convenient for clinical NLP applications. Furthermore, our findings indicate that when enough data is available, pre-training from scratch is better than continual pre-training when tested on clinical tasks, raising an exciting research question about which approach is optimal. Our models and fine-tuning scripts are publicly available at HuggingFace and GitHub.


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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 | Ona de Gibert Bonet | Carme Armentano-Oller | Aitor Gonzalez-Agirre | Maite Melero | Marta Villegas
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021


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Automatic Spanish Translation of SQuAD Dataset for Multi-lingual Question Answering
Casimiro Pio Carrino | Marta R. Costa-jussà | José A. R. Fonollosa
Proceedings of the Twelfth Language Resources and Evaluation Conference

Recently, multilingual question answering became a crucial research topic, and it is receiving increased interest in the NLP community. However, the unavailability of large-scale datasets makes it challenging to train multilingual QA systems with performance comparable to the English ones. In this work, we develop the Translate Align Retrieve (TAR) method to automatically translate the Stanford Question Answering Dataset (SQuAD) v1.1 to Spanish. We then used this dataset to train Spanish QA systems by fine-tuning a Multilingual-BERT model. Finally, we evaluated our QA models with the recently proposed MLQA and XQuAD benchmarks for cross-lingual Extractive QA. Experimental results show that our models outperform the previous Multilingual-BERT baselines achieving the new state-of-the-art values of 68.1 F1 on the Spanish MLQA corpus and 77.6 F1 on the Spanish XQuAD corpus. The resulting, synthetically generated SQuAD-es v1.1 corpora, with almost 100% of data contained in the original English version, to the best of our knowledge, is the first large-scale QA training resource for Spanish.


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Terminology-Aware Segmentation and Domain Feature for the WMT19 Biomedical Translation Task
Casimiro Pio Carrino | Bardia Rafieian | Marta R. Costa-jussà | José A. R. Fonollosa
Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2)

In this work, we give a description of the TALP-UPC systems submitted for the WMT19 Biomedical Translation Task. Our proposed strategy is NMT model-independent and relies only on one ingredient, a biomedical terminology list. We first extracted such a terminology list by labelling biomedical words in our training dataset using the BabelNet API. Then, we designed a data preparation strategy to insert the terms information at a token level. Finally, we trained the Transformer model with this terms-informed data. Our best-submitted system ranked 2nd and 3rd for Spanish-English and English-Spanish translation directions, respectively.