ACL 2023findings4 citations
Coherent or Not? Stressing a Neural Language Model for Discourse Coherence in Multiple Languages
Dominique Brunato, Felice Dell’Orletta, Irene Dini, Andrea Amelio Ravelli
Abstract
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.
BibTeX
@inproceedings{brunato-etal-2023-coherent,
title = "Coherent or Not? Stressing a Neural Language Model for Discourse Coherence in Multiple Languages",
author = "Brunato, Dominique and
Dell{'}Orletta, Felice and
Dini, Irene and
Ravelli, Andrea Amelio",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-acl.680/",
doi = "10.18653/v1/2023.findings-acl.680",
pages = "10690--10700"
}