Andrea Zaninello


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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 | Bernardo Magnini | Benjamin Molinet | Johana Ramirez-Romero | German Rigau | Jose Maria Villa-Gonzalez | Serena Villata | Andrea Zaninello
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

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 human-AI interaction. While these LLMs display competitive performance on automated medical texts benchmarks, they have been pre-trained and evaluated with a focus on a single language (English mostly). This is particularly true of text-to-text models, which typically require large amounts of domain-specific pre-training data, often not easily accessible for many languages. In this paper, we address these shortcomings by compiling, to the best of our knowledge, the largest multilingual corpus for the medical domain in four languages, namely English, French, Italian and Spanish. This new corpus has been used to train Medical mT5, the first open-source text-to-text multilingual model for the medical domain. Additionally, we present two new evaluation benchmarks for all four languages with the aim of facilitating multilingual research in this domain. A comprehensive evaluation shows that Medical mT5 outperforms both encoders and similarly sized text-to-text models for the Spanish, French, and Italian benchmarks, while being competitive with current state-of-the-art LLMs in English.


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A smashed glass cannot be full: Generation of Commonsense Explanations through Prompt-based Few-shot Learning
Andrea Zaninello | Bernardo Magnini
Proceedings of the 1st Workshop on Natural Language Reasoning and Structured Explanations (NLRSE)

We assume that providing explanations is a process to elicit implicit knowledge in human communication, and propose a general methodology to generate commonsense explanations from pairs of semantically related sentences. We take advantage of both prompting applied to large, encoder-decoder pre-trained language models, and few-shot learning techniques, such as pattern-exploiting training. Experiments run on the e-SNLI dataset show that the proposed method achieves state-of-the-art results on the explanation generation task, with a substantial reduction of labelled data. The obtained results open new perspective on a number of tasks involving the elicitation of implicit knowledge.


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Multiword Expression aware Neural Machine Translation
Andrea Zaninello | Alexandra Birch
Proceedings of the Twelfth Language Resources and Evaluation Conference

Multiword Expressions (MWEs) are a frequently occurring phenomenon found in all natural languages that is of great importance to linguistic theory, natural language processing applications, and machine translation systems. Neural Machine Translation (NMT) architectures do not handle these expressions well and previous studies have rarely addressed MWEs in this framework. In this work, we show that annotation and data augmentation, using external linguistic resources, can improve both translation of MWEs that occur in the source, and the generation of MWEs on the target, and increase performance by up to 5.09 BLEU points on MWE test sets. We also devise a MWE score to specifically assess the quality of MWE translation which agrees with human evaluation. We make available the MWE score implementation – along with MWE-annotated training sets and corpus-based lists of MWEs – for reproduction and extension.


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A Repository of Variation Patterns for Multiword Expressions
Malvina Nissim | Andrea Zaninello
Proceedings of the 9th Workshop on Multiword Expressions


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Creation of Lexical Resources for a Characterisation of Multiword Expressions in Italian
Andrea Zaninello | Malvina Nissim
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

The theoretical characterisation of multiword expressions (MWEs) is tightly connected to their actual occurrences in data and to their representation in lexical resources. We present three lexical resources for Italian MWEs, namely an electronic lexicon, a series of example corpora and a database of MWEs represented around morphosyntactic patterns. These resources are matched against, and created from, a very large web-derived corpus for Italian that spans across registers and domains. We can thus test expressions coded by lexicographers in a dictionary, thereby discarding unattested expressions, revisiting lexicographers's choices on the basis of frequency information, and at the same time creating an example sub-corpus for each entry. We organise MWEs on the basis of the morphosyntactic information obtained from the data in an electronic, flexible knowledge-base containing structured annotation exploitable for multiple purposes. We also suggest further work directions towards characterising MWEs by analysing the data organised in our database through lexico-semantic information available in WordNet or MultiWordNet-like resources, also in the perspective of expanding their set through the extraction of other similar compact expressions.