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.
Alessio Miaschi, A.A.R. (2022). Punctuation Restoration in Spoken Italian Transcripts with Transformers. GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND : Springer Science and Business Media Deutschland GmbH [10.1007/978-3-031-08421-8_17].
Punctuation Restoration in Spoken Italian Transcripts with Transformers
Andrea Amelio Ravelli;
2022
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
