3 citations · 5 across the 7 of their papers we have counts for
8 papers
Retrieval-Augmented Generation to Support Railways Engineering Tasks: A Case Study
Andrea Gerardo Russo, Federico Ruggeri, Ivan Tomarchio +5
The growing number and complexity of technical regulations represent an important challenge for all professionals in regulated industries. This paper describes a case study, from d…
TRI-DEP: A Trimodal Comparative Study for Depression Detection Using Speech, Text, and EEG
Annisaa Fitri Nurfidausi, Eleonora Mancini, Paolo Torroni
Depression is a widespread mental health disorder, yet its automatic detection remains challenging. Prior work has explored unimodal and multimodal approaches, with multimodal syst…
Leveraging Whisper Embeddings for Audio-based Lyrics Matching
Eleonora Mancini, Joan Serrà, Paolo Torroni +1
Audio-based lyrics matching can be an appealing alternative to other content-based retrieval approaches, but existing methods often suffer from limited reproducibility and inconsis…
Investigating the Effectiveness of Explainability Methods in Parkinson's Detection from Speech
Eleonora Mancini, Francesco Paissan, Paolo Torroni +2
Speech impairments in Parkinson's disease (PD) provide significant early indicators for diagnosis. While models for speech-based PD detection have shown strong performance, their i…
A Chatbot for Asylum-Seeking Migrants in Europe
Bettina Fazzinga, Elena Palmieri, Margherita Vestoso +4
We present ACME: A Chatbot for asylum-seeking Migrants in Europe. ACME relies on computational argumentation and aims to help migrants identify the highest level of protection they…
Let Guidelines Guide You: A Prescriptive Guideline-Centered Data Annotation Methodology
Federico Ruggeri, Eleonora Misino, Arianna Muti +3
We introduce the Guideline-Centered Annotation Methodology (GCAM), a novel data annotation methodology designed to report the annotation guidelines associated with each data sample…
Promoting the Responsible Development of Speech Datasets for Mental Health and Neurological Disorders Research
Eleonora Mancini, Ana Tanevska, Andrea Galassi +3
Current research in machine learning and artificial intelligence is largely centered on modeling and performance evaluation, less so on data collection. However, recent research de…
TWOLAR: a TWO-step LLM-Augmented distillation method for passage Reranking
Davide Baldelli, Junfeng Jiang, Akiko Aizawa +1
In this paper, we present TWOLAR: a two-stage pipeline for passage reranking based on the distillation of knowledge from Large Language Models (LLM). TWOLAR introduces a new scorin…