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HurtNet: a Multilingual Dictionary of Hurtful Words

Adel M. Wizani will present one of his recent collaborative works: HurtNet, a multilingual dictionary of hurtful terms, their senses, and examples of hurtful usage. The resource spans five languages: Arabic, French, Bulgarian, Greek, and Italian. The talk will outline the creation process for each language and the methods used for validation, focusing on the challenges of modeling hurtful language in Arabic (the presenter’s native language), particularly given its diglossic nature and semantic richness. It will also present results from a knowledge-injection zero-shot classification experiment, which demonstrate improved recall across languages, along with a qualitative analysis of model-generated explanations that reveal cross-linguistic patterns in the expression and detection of hurtful language.

Where: Sala Riunioni (1st floor)
When: 21/11/25 11:30

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Less than Meets the Eye: Representing Compounds in Large Language Models 

CCC Seminar Prof. Aline Villavicencio, Director of the Institute of Data Science and Artificial Intelligence, University of Exter, UK

Abstract

Large language models have been successfully used for capturing distinct (and very specific) word usages, and therefore could provide an attractive alternative for accurately determining meaning in language. However, these models still face a serious challenge when dealing with non-literal language, like that involved in Multiword Expressions (MWEs) such as idioms (make ends meet), light verb constructions (give a sigh), verb particle constructions (shake up) and noun compounds (loan shark). MWEs are an integral part of the mental lexicon of native speakers often used to express complex ideas in a simple and conventionalised way accepted by a given linguistic community. Although they may display a wealth of idiosyncrasies, from lexical, syntactic and semantic to statistical, that represents a real challenge for current LLMs, their accurate integration has the potential for improving the precision, naturalness and fluency of many tasks. In this talk, I will present an overview of how advances in LLMs have made an impact for the identification and modelling of idiomaticity and MWEs. I will concentrate on what models seem to incorporate of idiomaticity, as idiomatic interpretation may require knowledge that goes beyond what can be gathered from the individual words of an expression (e.g. “dark horse” as an unknown candidate who unexpectedly succeeds). I will also present an initiative to construct a multilingual idiomatic dataset.

Short Bio

Aline Villavicencio is the Director of the Institute of Data Science and Artificial Intelligence, University of Exeter, affiliated to the  Department of Computer Science, University of Sheffield (UK), is a member of ELLIS and has a Fellowship at the Alan Turing Institute. Before these, she held academic positions in the Institute of Informatics, Federal University of Rio Grande do Sul, Brazil (between 2005 and 2021) and in the School of Computer Science and Electronic Engineering, University of Essex, UK. She received her PhD from the University of Cambridge (UK) in 2001, and held postdoc positions at the University of Cambridge and University of Essex (UK). She was a Visiting Scholar at the Massachusetts Institute of Technology (USA, 2011-2012 and 2014-2015), at the École Normale Supé­rieure (France, 2014), an Erasmus-Mundus Visting Scholar at Saarland University (Germany in 2012/2013) and at the University of Bath (UK, 2006-2009).  She held a Research Fellowship from the  Brazilian National Council for Scientific and Technological Development (Brazil, 2009-2017). She is a member of the editorial board of Computational Linguistics, TACL and of JNLE. She is the General Chair of EACL 2026 and was the PC Co-Chair of ACL 2022, CoNLL 2019, Senior Area Chair for EMNLP 2025, ACL 2020 and ACL 2019 among others and General co-chair for the 2018 International Conference on Computational Processing of Portuguese. She was also a member of the NAACL board, SIGLEX board and of the program committees of various *ACL and AI conferences, and has co-chaired  several *ACL workshops on Cognitive Aspects of Computational Language Acquisition and on Multiword Expressions. Her research interests include lexical semantics, multilinguality, multiword expressions and cognitively motivated NLP, and has co-edited special issues and books dedicated to these topics.

When:  13/11/25 , h 11.00

Where:  Sala Conferenze – 3rd floor

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Bootstrapping UMRs from UD for Scalable Multilingual Annotation

CCC Seminar by Federica Gamba – PhD student in Computational Linguistics, Charles University, Czech Republic

Abstract

Uniform Meaning Representation (UMR) offers a cross-linguistically applicable framework for capturing sentence- and document-level semantics, but producing UMR annotations from scratch is a time-intensive process. In this talk, I will present an approach for bootstrapping UMR graphs by leveraging Universal Dependencies (UD), a richly annotated multilingual syntactic resource covering a wide range of language families. I will describe how structural correspondences between UD and UMR can be exploited to automatically derive partial UMR graphs from UD trees, providing annotators with an initial representation to refine rather than create from scratch. While UD is not inherently semantic, it encodes syntactic information that maps well onto UMR structures, allowing us to extract meaningful correspondences that simplify annotation. This method not only reduces annotation effort but also facilitates scalable UMR creation across typologically diverse languages, aligning with UMR’s cross-linguistic design goals.

When:  12/11/25 , h 9.00

Where:  Sala Conferenze – 3rd floor