Punctuation Restoration in Spoken Italian Transcripts with Transformers

@InProceedings{10.1007/978-3-031-08421-8_17,
author="Miaschi, Alessio
and Ravelli, Andrea Amelio
and Dell'Orletta, Felice",
editor="Bandini, Stefania
and Gasparini, Francesca
and Mascardi, Viviana
and Palmonari, Matteo
and Vizzari, Giuseppe",
title="Punctuation Restoration in Spoken Italian Transcripts with Transformers",
booktitle="AIxIA 2021 -- Advances in Artificial Intelligence",
year="2022",
publisher="Springer International Publishing",
address="Cham",
pages="245--260",
abstract="In this paper, we propose an evaluation of a Transformer-based punctuation restoration model for the Italian language. Experimenting with a BERT-base model, we perform several fine-tuning with different training data and sizes and tested them in an in- and cross-domain scenario. Moreover, we conducted an error analysis of the main weaknesses of the model related to specific punctuation marks. Finally, we test our system either quantitatively and qualitatively, by offering a typical task-oriented and a perception-based acceptability evaluation.",
isbn="978-3-031-08421-8"
}