1 citations · 5 across the 10 of their papers we have counts for
10 papers
EMERGE: A Benchmark for Updating Knowledge Graphs with Emerging Textual Knowledge
Klim Zaporojets, Daniel Daza, Edoardo Barba +3
Knowledge Graphs (KGs) are structured knowledge repositories containing entities and relations between them. In this paper, we study the problem of automatically updating KGs over…
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…
Beyond Correlation: Interpretable Evaluation of Machine Translation Metrics
Stefano Perrella, Lorenzo Proietti, Pere-Lluís Huguet Cabot +2
Machine Translation (MT) evaluation metrics assess translation quality automatically. Recently, researchers have employed MT metrics for various new use cases, such as data filteri…
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…
ReLiK: Retrieve and LinK, Fast and Accurate Entity Linking and Relation Extraction on an Academic Budget
Riccardo Orlando, Pere-Lluis Huguet Cabot, Edoardo Barba +1
Entity Linking (EL) and Relation Extraction (RE) are fundamental tasks in Natural Language Processing, serving as critical components in a wide range of applications. In this paper…
Maverick: Efficient and Accurate Coreference Resolution Defying Recent Trends
Giuliano Martinelli, Edoardo Barba, Roberto Navigli
Large autoregressive generative models have emerged as the cornerstone for achieving the highest performance across several Natural Language Processing tasks. However, the urge to…
Code-Switching with Word Senses for Pretraining in Neural Machine Translation
Vivek Iyer, Edoardo Barba, Alexandra Birch +2
Lexical ambiguity is a significant and pervasive challenge in Neural Machine Translation (NMT), with many state-of-the-art (SOTA) NMT systems struggling to handle polysemous words…
Semantic Role Labeling Meets Definition Modeling: Using Natural Language to Describe Predicate-Argument Structures
Simone Conia, Edoardo Barba, Alessandro Scirè +1
One of the common traits of past and present approaches for Semantic Role Labeling (SRL) is that they rely upon discrete labels drawn from a predefined linguistic inventory to clas…
Entity Disambiguation with Entity Definitions
Luigi Procopio, Simone Conia, Edoardo Barba +1
Local models have recently attained astounding performances in Entity Disambiguation (ED), with generative and extractive formulations being the most promising research directions.…