Contributo in atti di convegno, 2023, ENG, 10.18653/v1/2023.findings-acl.680
Dominique Brunato; Felice Dell'Orletta; Irene Dini; Andrea Amelio Ravelli
Istituto di Linguistica Computazionale "Antonio Zampolli", Pisa; Istituto di Linguistica Computazionale "Antonio Zampolli", Pisa; Istituto di Linguistica Computazionale "Antonio Zampolli", Pisa/ University of Pisa; Istituto di Linguistica Computazionale ILC-CNR / University of Bologna
In this study, we investigate the capability of a Neural Language Model (NLM) to distinguish between coherent and incoherent text, where the latter has been artificially created to gradually undermine local coherence within text. While previous research on coherence assessment using NLMs has primarily focused on English, we extend our investigation to multiple languages. We employ a consistent evaluation framework to compare the performance of monolingual and multilingual models in both in-domain and out-domain settings. Additionally, we explore the model's performance in a cross-language scenario.
61st Annual Meeting of the Association for Computational Linguistics (ACL 2023), pp. 10690–10700, Toronto, Canada, 9-14/07/2023
text coherence, neural language models, multilingual corpora
Ravelli Andrea Amelio, Dini Irene, Dell Orletta Felice, Brunato Dominique Pierina
ILC – Istituto di linguistica computazionale "Antonio Zampolli"
ID: 491078
Year: 2023
Type: Contributo in atti di convegno
Creation: 2024-01-03 13:47:53.000
Last update: 2024-01-10 11:02:52.000
External IDs
CNR OAI-PMH: oai:it.cnr:prodotti:491078
DOI: 10.18653/v1/2023.findings-acl.680