The collection of historical notarial documentation from Bologna is a valuable source, providing deep insights into the city’s institutional, legal, and socio-economic history. However, many of these manuscripts have sustained physical damage during centuries of conservation, rendering the text incomplete. To address this, we explored the restoration of these Latin notary documents using encoder-based pre-trained language models (PLMs) under the assumption that the length of missing text is known by estimation from the physical damage. We address the structural misalignment between the physical lacuna of the manuscript and the subword tokenization schemes of PLMs by designing an iterative decoding strategy to align model predictions with the known physical dimensions of lacuna. We also compared the efficacy of monolingual versus multilingual pre-training. Our strategy significantly outperforms baselines consist of standard decoding methods. Furthermore, stratified analysis across different text sections reveals that while monolingual models achieve better performance in general, multilingual models show a suggestive advantage in lexically dense segments, though this finding is not statistically significant. Overall, the best performance achieved by our method is a Hit@1 rate of 35.47% in the short-span setting and 18.75% in the long-span setting. While fully autonomous restoration remains an open challenge, our system provides a useful assistive tool for paleographers.

Voice Activity Detection (VAD) refers to the task of identifying human voice activity in noisy settings, playing a crucial role in fields like speech recognition and audio surveillance. However, most VAD research focuses on English, leaving other languages, such as Italian, under-explored. This study aims to evaluate and enhance VAD systems for Italian speech, with the goal of finding a solution for the speech segmentation component of the Digital Linguistic Biomarkers (DLBs) extraction pipeline for early mental disorder diagnosis. We experimented with various VAD systems and propose an ensemble VAD system that integrates the best-performing models. Our ensemble system shows significant improvements in speech event detection. This advancement lays a robust foundation for more accurate early detection of mental health issues using DLBs in Italian.

Given a word in context, the task of VisualWord Sense Disambiguation consists of select-ing the correct image among a set of candidates. To select the correct image, we propose a so-lution blending text augmentation and multi-modal models. Text augmentation leverages thefine-grained semantic annotation from Word-Net to get a better representation of the tex-tual component. We then compare this sense-augmented text to the set of image using pre-trained multimodal models CLIP and ViLT. Oursystem has been ranked 16th for the Englishlanguage, achieving 68.5 points for hit rate and79.2 for mean reciprocal rank.