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Bernardo Magnini

4 accepted papers

2025

All-in-one: Understanding and Generation in Multimodal Reasoning with the MAIA Benchmark

Davide Testa, Giovanni Bonetta, Raffaella Bernardi, Alessandro Bondielli, Alessandro Lenci, Alessio Miaschi +2
EMNLP 2025

We introduce MAIA (Multimodal AI Assessment), a native-Italian benchmark designed for fine-grained investigation of the reasoning abilities of visual language models on videos. MAIA differs from other available video benchmarks for its design, its reasoning categories, the metric it uses, and the la

Cited by 0Source ↗PDF ↗code ↗Scholar ↗
2024

MedMT5: An Open-Source Multilingual Text-to-Text LLM for the Medical Domain

Iker García-Ferrero, Rodrigo Agerri, Aitziber Atutxa Salazar, Elena Cabrio, Iker de la Iglesia, Alberto Lavelli +7
COLING 2024main

Research on language technology for the development of medical applications is currently a hot topic in Natural Language Understanding and Generation. Thus, a number of large language models (LLMs) have recently been adapted to the medical domain, so that they can be used as a tool for mediating in…

Cited by 44Source ↗PDF ↗Scholar ↗
2024

Towards Cost-effective Multi-style Conversations: A Pilot Study in Task-oriented Dialogue Generation

Tiziano Labruna, Bernardo Magnini
COLING 2024main

Conversations exhibit significant variation when different styles are employed by participants, often leading to subpar performance when a dialogue model is exclusively trained on single-style datasets. We present a cost-effective methodology for generating multi-style conversations, which can be us…

Cited by 0Source ↗PDF ↗code ↗Scholar ↗
2020

Recent Neural Methods on Slot Filling and Intent Classification for Task-Oriented Dialogue Systems: A Survey

Samuel Louvan, Bernardo Magnini
COLING 2020main

In recent years, fostered by deep learning technologies and by the high demand for conversational AI, various approaches have been proposed that address the capacity to elicit and understand user’s needs in task-oriented dialogue systems. We focus on two core tasks, slot filling (SF) and intent clas…

Cited by 143Source ↗PDF ↗Scholar ↗