Papers by German Rigau
Building Named Entity Recognition Taggers via Parallel Corpora (L18-1)
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| Challenge: | Existing methods to generate semantic processors for languages lacking hand curated data are inefficiently slow and unaffordable in terms of human resources and economic costs. |
| Approach: | They propose to use statistical word alignments to project annotations from multiple sources to a target language. |
| Outcome: | The proposed method is effective to transport NER annotations across languages . it can generate a good statistical model for a new target language . |
Cross-checking WordNet and SUMO Using Meronymy (L18-1)
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| Challenge: | Existing methods to validate knowledge encoded in WordNet, SUMO and their mapping have been mainly manual . |
| Approach: | They propose to use WordNet and SUMO to validate knowledge by using automated theorem provers to evaluate the competency of SU MO-based ontologies. |
| Outcome: | The proposed method enables validation of some pieces of information and also the detection of missing information or inconsistencies among knowledge resources. |
MedMT5: An Open-Source Multilingual Text-to-Text LLM for the Medical Domain (2024.lrec-main)
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Iker García-Ferrero, Rodrigo Agerri, Aitziber Atutxa Salazar, Elena Cabrio, Iker de la Iglesia, Alberto Lavelli, Bernardo Magnini, Benjamin Molinet, Johana Ramirez-Romero, German Rigau, Jose Maria Villa-Gonzalez, Serena Villata, Andrea Zaninello
| Challenge: | Existing studies on large language models for medical applications have focused on a single language . medical mT5 outperforms both encoders and similar sized text-to-text models in English, French, and Italian benchmarks . |
| Approach: | They propose to train Medical mT5, the first open-source text-to-text multilingual model for the medical domain. |
| Outcome: | The proposed model outperforms encoders and similar sized models on the Spanish, French, and Italian benchmarks while being competitive with current state-of-the-art models in English. |
Machine Translation for Low-Resource Languages through Monolingual Data and LLM: A Case Study of English-to-Basque (2026.eacl-srw)
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| Challenge: | Existing LLMs do not translate well from English to Basque, but they yield an acceptable performance in the reverse direction. |
| Approach: | They propose to use a Basque monolingual corpora to train an LLM-based MT system . they use 'sovereignty fine tuning' to generate parallel corporata, and then use preference optimization . |
| Outcome: | The proposed system improves translation quality in English-to-Basque direction while requiring limited data for low-resource languages. |
Developing New Linguistic Resources and Tools for the Galician Language (L18-1)
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| Challenge: | Existing resources and tools for the Galician language are lacking for other less-resourced languages, such as statistical tools for lemmatization and Named Entity Recognition. |
| Approach: | They propose to develop a manually revised corpus for POS tagging and lemmatization, and a new manually annotated corpus to train existing statistical tools for the Galician language. |
| Outcome: | The proposed resources include a new corpus for POS tagging and lemmatization, and a manually annotated corpus to handle Named Entity recognition. |
Biomedical term normalization of EHRs with UMLS (L18-1)
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| Challenge: | Currently, there is no tool for this language and for this specific purpose. |
| Approach: | They propose a multilingual and cross-lingual tool that searches for biomedical terms in clinical texts with the Unified Medical Language System (UMLS) Metathesaurus. |
| Outcome: | The proposed tool performs biomedical term normalization in clinical texts with the unified medical language system (UMLS) Metathesaurus . it is based on Apache Lucene TM and is available on-line 2 . |
Can LLMs Reason Like Doctors? Exploring the Limits of Large Language Models in Complex Medical Reasoning (2026.findings-eacl)
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| Challenge: | Large language models (LLMs) have shown remarkable progress in reasoning across multiple domains, but it remains unclear whether their abilities reflect genuine reasoning or sophisticated pattern matching. |
| Approach: | They conduct one of the largest evaluations to date, assessing 77 LLMs . they select three medical question answering (QA) benchmarks targeting reasoning processes . |
| Outcome: | The results highlight the need to improve specific reasoning strategies to better reflect medical decision-making. |
This is not a Dataset: A Large Negation Benchmark to Challenge Large Language Models (2023.emnlp-main)
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| Challenge: | Large language models (LLMs) have grammatical knowledge but fail to interpret negation . a recent study shows that LLMs struggle with negative sentences . |
| Approach: | They propose to use a dataset to grasp LLMs' generalization and inference capability . they also fine-tuned models to assess whether the understanding of negation can be trained . |
| Outcome: | The proposed model is able to generalize and infer negation in 400,000 sentences . but it is suboptimal when it comes to negation, a key step in natural language processing . |
Multilingual Stance Detection in Tweets: The Catalonia Independence Corpus (2020.lrec-1)
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| Challenge: | stance detection is a method to determine the attitude of a text with respect to a specific topic or claim. |
| Approach: | They propose a multilingual dataset for stance detection in Twitter for the Catalan and Spanish languages. |
| Outcome: | The proposed dataset shows that it is well balanced for multilingual and cross-lingual research. |
NUBes: A Corpus of Negation and Uncertainty in Spanish Clinical Texts (2020.lrec-1)
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| Challenge: | Currently, there are not many corpora that cover propositional aspects of meaning . these include factuality, uncertainty, opinions, beliefs, intentions or subjectivity . |
| Approach: | They introduce the first version of the NUBes corpus (Negation and Uncertainty annotations in Biomedical texts in Spanish) . it includes an exhaustive comparison with similar corpora in Spanish and preliminary experiments using deep learning algorithms to validate the annotated dataset. |
| Outcome: | The proposed corpus is compared with similar corpora in Spanish and performs preliminary experiments using deep learning algorithms. |
T-Projection: High Quality Annotation Projection for Sequence Labeling Tasks (2023.findings-emnlp)
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| Challenge: | Annotation projection is a task of transporting labels from source to target language without high-quality training data. |
| Approach: | They propose an annotation projection approach that leverages pretrained text2text models and machine translation technology to generate annotated data. |
| Outcome: | The proposed approach outperforms existing methods on intrinsic and extrinsic tasks in 5 Indo-European and 8 low-resource African languages. |
Benchmarking Meta-embeddings: What Works and What Does Not (2021.findings-emnlp)
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| Challenge: | Existing methods to build meta-embeddings have been evaluated using a variety of methods and datasets, which makes it difficult to draw meaningful conclusions regarding the merits of each approach. |
| Approach: | They propose a unified framework for a fair and objective meta-embedding evaluation using intrinsic and extrinsic tasks. |
| Outcome: | The proposed framework outperforms existing methods on intrinsic and extrinsic evaluation benchmarks and outperformed existing methods. |
ClinIDMap: Towards a Clinical IDs Mapping for Data Interoperability (2022.lrec-1)
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| Challenge: | a tool for mapping identifiers between clinical ontologies and lexical resources is available. |
| Approach: | They propose a tool for mapping identifiers between clinical ontologies and lexical resources. |
| Outcome: | The proposed mapping tool can be used to enrich existing annotated corpora in multiple languages. |
Model and Data Transfer for Cross-Lingual Sequence Labelling in Zero-Resource Settings (2022.findings-emnlp)
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| Challenge: | Existing studies have proposed data-based cross-lingual transfer as an effective technique for cross-linguistic sequence labelling, but they have failed to perform well. |
| Approach: | They propose to use data-based cross-lingual transfer to train supervised models from a source language to unlabelled target languages. |
| Outcome: | The proposed techniques outperform data-based cross-lingual transfer approaches in a zero-shot setting. |
IberoBench: A Benchmark for LLM Evaluation in Iberian Languages (2025.coling-main)
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Irene Baucells, Javier Aula-Blasco, Iria de-Dios-Flores, Silvia Paniagua Suárez, Naiara Perez, Anna Salles, Susana Sotelo Docio, Júlia Falcão, Jose Javier Saiz, Robiert Sepulveda Torres, Jeremy Barnes, Pablo Gamallo, Aitor Gonzalez-Agirre, German Rigau, Marta Villegas
| Challenge: | Existing multi-task benchmarks for Large Language Models are limited to English . a new benchmark is needed to evaluate models on a range of tasks . |
| Approach: | They propose a multilingual, multi-task benchmark for Iberian languages built on the LM Evaluation Harness framework. |
| Outcome: | The proposed benchmark covers 62 tasks divided into 179 subtasks and is available in Iberian, Basque, Catalan, Galician, European Spanish and European Portuguese. |
Instructing Large Language Models for Low-Resource Languages: A Systematic Study for Basque (2025.emnlp-main)
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Oscar Sainz, Naiara Perez, Julen Etxaniz, Joseba Fernandez de Landa, Itziar Aldabe, Iker García-Ferrero, Aimar Zabala, Ekhi Azurmendi, German Rigau, Eneko Agirre, Mikel Artetxe, Aitor Soroa
| Challenge: | Instructing language models with user intent requires large instruction datasets limited to a limited set of languages. |
| Approach: | They propose to use existing LLMs and synthetically generated instructions to train models with user intent. |
| Outcome: | The proposed model outperforms base non-instructed models on Basque without Basque instructions. |