3 citations · 4 across the 5 of their papers we have counts for
12 papers · 1 filter
LiteraryQA: Towards Effective Evaluation of Long-document Narrative QA
Tommaso Bonomo, Luca Gioffré, Roberto Navigli
Question Answering (QA) on narrative text poses a unique challenge to current systems, requiring a deep understanding of long, complex documents. However, the reliability of Narrat…
Do Large Language Models Understand Word Senses?
Domenico Meconi, Simone Stirpe, Federico Martelli +2
Understanding the meaning of words in context is a fundamental capability for Large Language Models (LLMs). Despite extensive evaluation efforts, the extent to which LLMs show evid…
Has Machine Translation Evaluation Achieved Human Parity? The Human Reference and the Limits of Progress
Lorenzo Proietti, Stefano Perrella, Roberto Navigli
In Machine Translation (MT) evaluation, metric performance is assessed based on agreement with human judgments. In recent years, automatic metrics have demonstrated increasingly hi…
Optimizing LLMs for Italian: Reducing Token Fertility and Enhancing Efficiency Through Vocabulary Adaptation
Luca Moroni, Giovanni Puccetti, Pere-Lluis Huguet Cabot +6
The number of pretrained Large Language Models (LLMs) is increasing steadily, though the majority are designed predominantly for the English language. While state-of-the-art LLMs c…
Word Sense Linking: Disambiguating Outside the Sandbox
Andrei Stefan Bejgu, Edoardo Barba, Luigi Procopio +2
Word Sense Disambiguation (WSD) is the task of associating a word in a given context with its most suitable meaning among a set of possible candidates. While the task has recently…
Guardians of the Machine Translation Meta-Evaluation: Sentinel Metrics Fall In!
Stefano Perrella, Lorenzo Proietti, Alessandro Scirè +2
Annually, at the Conference of Machine Translation (WMT), the Metrics Shared Task organizers conduct the meta-evaluation of Machine Translation (MT) metrics, ranking them according…