Papers by Jose Manuel Gomez-Perez
ISAAQ - Mastering Textbook Questions with Pre-trained Transformers and Bottom-Up and Top-Down Attention (2020.emnlp-main)
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| Challenge: | Textbook Question Answering is a complex task that requires reasoning with multimodal information from text and diagrams. |
| Approach: | They propose to use transformer language models and bottom-up and top-down attention to tackle the language and visual understanding challenges of text and diagrams. |
| Outcome: | The proposed system achieves unprecedented accuracies on all TQA question types . the system also obtains state-of-the-art results in other demanding datasets . |
European Language Grid: A Joint Platform for the European Language Technology Community (2021.eacl-demos)
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Georg Rehm, Stelios Piperidis, Kalina Bontcheva, Jan Hajic, Victoria Arranz, Andrejs Vasiļjevs, Gerhard Backfried, Jose Manuel Gomez-Perez, Ulrich Germann, Rémi Calizzano, Nils Feldhus, Stefanie Hegele, Florian Kintzel, Katrin Marheinecke, Julian Moreno-Schneider, Dimitris Galanis, Penny Labropoulou, Miltos Deligiannis, Katerina Gkirtzou, Athanasia Kolovou, Dimitris Gkoumas, Leon Voukoutis, Ian Roberts, Jana Hamrlova, Dusan Varis, Lukas Kacena, Khalid Choukri, Valérie Mapelli, Mickaël Rigault, Julija Melnika, Miro Janosik, Katja Prinz, Andres Garcia-Silva, Cristian Berrio, Ondrej Klejch, Steve Renals
| Challenge: | Europe is a multilingual society, in which dozens of languages are spoken. |
| Approach: | They describe the European Language Grid, which is targeted to evolve into the primary platform and marketplace for LT in Europe by providing one umbrella platform for the European LT landscape. |
| Outcome: | The European Language Grid (ELG) will provide access to 1300 services for all European languages as well as thousands of data sets. |
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. |
SPACE-IDEAS: A Dataset for Salient Information Detection in Space Innovation (2024.lrec-main)
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| Challenge: | Detecting salient parts in text is widely used to mitigate information overload. |
| Approach: | They propose a dataset for salient information detection from space innovation that is manually annotated using a large generative language model. |
| Outcome: | The proposed dataset can be leveraged using multitask learning to train better classifiers. |
SciClaims: An End-to-End Generative System for Biomedical Claim Analysis (2025.emnlp-demos)
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| Challenge: | SciClaims is an interactive web-based system for scientific claim analysis in the biomedical domain. |
| Approach: | They present SciClaims, an interactive web-based system for scientific claim analysis in the biomedical domain. |
| Outcome: | The system extracts factual claims from scientific texts and retrieves evidence from PubMed . it also verifies the validity of each claim using large language models . the system is optimized to run efficiently on a single GPU and is publicly available . |