Papers by Cesare Campagnano
SRL4E – Semantic Role Labeling for Emotions: A Unified Evaluation Framework (2022.acl-long)
Copied to clipboard
| Challenge: | Existing datasets for emotion detection are heterogeneous in size, domain, format, splits, emotion categories and role labels, hampering progress in this area. |
| Approach: | They propose a framework for annotating emotions manually using a common labeling scheme to unify several datasets tagged with emotions and semantic roles. |
| Outcome: | The proposed framework unifies datasets tagged with emotions and semantic roles by using a common labeling scheme. |
Statistical Foundations of DIME: Risk Estimation for Practical Index Selection (2026.eacl-short)
Copied to clipboard
Giulio D'Erasmo, Cesare Campagnano, Antonio Mallia, Pierpaolo Brutti, Nicola Tonellotto, Fabrizio Silvestri
| Challenge: | High-dimensional dense embeddings are noisy or redundant, causing performance degradation and causing errors. |
| Approach: | They propose a method that scores each dimension by fusing the embeddings into a query-dependent matrix. |
| Outcome: | The proposed method improves retrieval effectiveness and reduces embedding size by an average 50% of across different models and datasets at inference time. |
DanteLLM: Let’s Push Italian LLM Research Forward! (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing models for large language processing in the English language are limited in resources and evaluation tools for non-English languages. |
| Approach: | They propose a benchmark and an open LLM Leaderboard to evaluate LLMs’ performance in Italian and propose 'DanteLLM' it is the most performant LLM in the world, with improvements of up to 6 points . |
| Outcome: | The proposed model outperforms existing models in Italian and offers a blueprint for the development and evaluation of LLMs in other languages. |