Papers by Bernardo Magnini
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. |
The European Language Technology Landscape in 2020: Language-Centric and Human-Centric AI for Cross-Cultural Communication in Multilingual Europe (2020.lrec-1)
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Georg Rehm, Katrin Marheinecke, Stefanie Hegele, Stelios Piperidis, Kalina Bontcheva, Jan Hajič, Khalid Choukri, Andrejs Vasiļjevs, Gerhard Backfried, Christoph Prinz, José Manuel Gómez-Pérez, Luc Meertens, Paul Lukowicz, Josef van Genabith, Andrea Lösch, Philipp Slusallek, Morten Irgens, Patrick Gatellier, Joachim Köhler, Laure Le Bars, Dimitra Anastasiou, Albina Auksoriūtė, Núria Bel, António Branco, Gerhard Budin, Walter Daelemans, Koenraad De Smedt, Radovan Garabík, Maria Gavriilidou, Dagmar Gromann, Svetla Koeva, Simon Krek, Cvetana Krstev, Krister Lindén, Bernardo Magnini, Jan Odijk, Maciej Ogrodniczuk, Eiríkur Rögnvaldsson, Mike Rosner, Bolette Pedersen, Inguna Skadiņa, Marko Tadić, Dan Tufiș, Tamás Váradi, Kadri Vider, Andy Way, François Yvon
| Challenge: | Language Technologies (LTs) are a powerful means to break down language barriers impacting business, cross-lingual and cross-cultural communication in Europe. |
| Approach: | They present an overview of the European LT landscape and the current state of play in industry and the LT market. |
| Outcome: | The present study outlines funding programmes, activities, actions and challenges in the different countries with regard to LT, including the current state of play in industry and the LT market. |
Thesis Proposal: LLMs post-training for multilingual medical tasks. Instruction-Tuning, Continual-Pretraining or Reasoning? (2026.acl-srw)
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| Challenge: | Adapting Large Language Models to the medical domain remains an active area of research . |
| Approach: | They propose to compare three common adaptation approaches to adapt large language models to the medical domain. |
| Outcome: | The proposed models are built on top of foundational LLMs and rely on different post-training methodologies for domain and task performance. |
FASTDial: Abstracting Dialogue Policies for Fast Development of Task Oriented Agents (P19-3)
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| Challenge: | Existing tools for creating task oriented dialogue agents are very flexible and require domain expertise to design them. |
| Approach: | They propose a framework for task oriented dialogue agents built on top of the OpenDial toolkit. |
| Outcome: | The proposed framework reduces programming effort and domain expert training time by hiding many implementation details. |
Addressing Domain Changes in Task-oriented Conversational Agents through Dialogue Adaptation (2023.eacl-srw)
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| Challenge: | Recent task-oriented dialogue systems are trained on annotated dialogues, but when domain knowledge changes, the initial model may become obsolete. |
| Approach: | They propose to use an annotated dialogue dataset to train a dialogue model for domain changes . they propose to fine-tune a generative language model on domain changes to reduce performance . |
| Outcome: | The proposed approach reduces performance by 55% by fine-tuning a generative language model on domain changes. |
Towards Cost-effective Multi-style Conversations: A Pilot Study in Task-oriented Dialogue Generation (2024.lrec-main)
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| Challenge: | Current task-oriented dialogue systems are trained on a single conversational style and do not account for the diversity of styles encountered when interacting with different users. |
| Approach: | They propose a method for generating multi-style conversations using a multi-language dataset that is available in a conversational domain. |
| Outcome: | The proposed model can be used in the development of conversational agents . it assumes the availability of a conversational domain and leverages the generative capabilities of large language models. |
All-in-one: Understanding and Generation in Multimodal Reasoning with the MAIA Benchmark (2025.findings-emnlp)
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Davide Testa, Giovanni Bonetta, Raffaella Bernardi, Alessandro Bondielli, Alessandro Lenci, Alessio Miaschi, Lucia Passaro, Bernardo Magnini
| Challenge: | MAIA evaluates visual language models on video-related tasks using reasoning categories that aim to disentangle language and vision relations. |
| Approach: | a native-italian benchmark is designed for fine-grained investigation of the reasoning abilities of visual language models on videos. |
| Outcome: | The benchmark evaluates visual language models on two aligned tasks and a visual question-answering task. |
Recent Neural Methods on Slot Filling and Intent Classification for Task-Oriented Dialogue Systems: A Survey (2020.coling-main)
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| Challenge: | In recent years, neural-network based models have been used for a wide range of tasks, including slot filling and intent classification. |
| Approach: | They propose three neural architectures to model slot filling and intent classification . they propose independent models, joint models and transfer learning models that exploit the mutual benefit of the two tasks simultaneously and scale the model to new domains. |
| Outcome: | The proposed models model SF and IC separately, exploit mutual benefit of the two tasks simultaneously and scale the model to new domains. |
Enriching a Lexicon of Discourse Connectives with Corpus-based Data (L18-1)
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| Challenge: | Existing annotation efforts for multiple languages have focused on discourse connectives, but we have limited it to the class of connectives marking contrast and the additional relations such connectives might convey. |
| Approach: | They enrich a lexicon of italian COnnectives with real corpus data for connectives marking contrast relations in text. |
| Outcome: | The proposed resource is a valuable tool for linguistic analyses of discourse relations and the training of a classifier for NLP applications. |
Dynamic Task-Oriented Dialogue: A Comparative Study of Llama-2 and Bert in Slot Value Generation (2024.eacl-srw)
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| Challenge: | Recent advances in instruction-based language models have demonstrated exceptional performance across various natural language processing tasks. |
| Approach: | They propose to use BERT and Llama-2 to generate dynamic task-oriented dialogues using a multi-dimensional dataset. |
| Outcome: | The proposed models generate predictions for masked slot values within text and are reproducible in open-source environments. |
KRAUTS: A German Temporally Annotated News Corpus (L18-1)
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| Challenge: | Temporal tagging is an important task towards improved natural language understanding. |
| Approach: | They present a new German temporally annotated corpus with 192 documents with 1,140 annotations . they propose to make temporal tagging a viable research area . |
| Outcome: | The proposed corpus contains 192 documents with 1,140 annotated temporal expressions. |
Comparing Machine Learning and Deep Learning Approaches on NLP Tasks for the Italian Language (2020.lrec-1)
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| Challenge: | Using available datasets, we compare deep learning and traditional machine learning methods for various NLP tasks in Italian. |
| Approach: | They compare deep learning and traditional machine learning methods for various NLP tasks in Italian. |
| Outcome: | The proposed methods outperform traditional methods in sequence tagging tasks and classification tasks in Italian. |