@inproceedings{gullace-etal-2024-self,
title = "The Self-Contained {I}talian Negation Test ({SCIN})",
author = "Gullace, Viola and
Kletz, David and
Poibeau, Thierry and
Lenci, Alessandro and
Amsili, Pascal",
editor = "Dell'Orletta, Felice and
Lenci, Alessandro and
Montemagni, Simonetta and
Sprugnoli, Rachele",
booktitle = "Proceedings of the 10th Italian Conference on Computational Linguistics (CLiC-it 2024)",
month = dec,
year = "2024",
address = "Pisa, Italy",
publisher = "CEUR Workshop Proceedings",
url = "https://aclanthology.org/2024.clicit-1.51/",
pages = "425--430",
ISBN = "979-12-210-7060-6",
abstract = "Recent research has focused extensively on state-of-the-art pretrained language models, particularly those based on Transformer architectures, and how well they account for negation and other linguistic phenomena in various tasks. This study aims to evaluate the understanding of negation in Italian bert- and roberta-based models, contrasting the predominant English-focused prior research. We develop the SCIN Set, an Italian dataset designed to model the influence of polarity constraints on models in a masked predictions task. Applying the SCIN Set reveals that these models do not adjust their behaviour based on sentences polarity, even when the resulting sentence is contradictory. We conclude that the tested models lack a clear understanding of how negation alters sentence meaning."
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<abstract>Recent research has focused extensively on state-of-the-art pretrained language models, particularly those based on Transformer architectures, and how well they account for negation and other linguistic phenomena in various tasks. This study aims to evaluate the understanding of negation in Italian bert- and roberta-based models, contrasting the predominant English-focused prior research. We develop the SCIN Set, an Italian dataset designed to model the influence of polarity constraints on models in a masked predictions task. Applying the SCIN Set reveals that these models do not adjust their behaviour based on sentences polarity, even when the resulting sentence is contradictory. We conclude that the tested models lack a clear understanding of how negation alters sentence meaning.</abstract>
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%0 Conference Proceedings
%T The Self-Contained Italian Negation Test (SCIN)
%A Gullace, Viola
%A Kletz, David
%A Poibeau, Thierry
%A Lenci, Alessandro
%A Amsili, Pascal
%Y Dell’Orletta, Felice
%Y Lenci, Alessandro
%Y Montemagni, Simonetta
%Y Sprugnoli, Rachele
%S Proceedings of the 10th Italian Conference on Computational Linguistics (CLiC-it 2024)
%D 2024
%8 December
%I CEUR Workshop Proceedings
%C Pisa, Italy
%@ 979-12-210-7060-6
%F gullace-etal-2024-self
%X Recent research has focused extensively on state-of-the-art pretrained language models, particularly those based on Transformer architectures, and how well they account for negation and other linguistic phenomena in various tasks. This study aims to evaluate the understanding of negation in Italian bert- and roberta-based models, contrasting the predominant English-focused prior research. We develop the SCIN Set, an Italian dataset designed to model the influence of polarity constraints on models in a masked predictions task. Applying the SCIN Set reveals that these models do not adjust their behaviour based on sentences polarity, even when the resulting sentence is contradictory. We conclude that the tested models lack a clear understanding of how negation alters sentence meaning.
%U https://aclanthology.org/2024.clicit-1.51/
%P 425-430
Markdown (Informal)
[The Self-Contained Italian Negation Test (SCIN)](https://aclanthology.org/2024.clicit-1.51/) (Gullace et al., CLiC-it 2024)
ACL
- Viola Gullace, David Kletz, Thierry Poibeau, Alessandro Lenci, and Pascal Amsili. 2024. The Self-Contained Italian Negation Test (SCIN). In Proceedings of the 10th Italian Conference on Computational Linguistics (CLiC-it 2024), pages 425–430, Pisa, Italy. CEUR Workshop Proceedings.