The task of automatically evaluating acceptability judgments has relished increasing success in Natural Language Processing, starting from including the Corpus of Linguistic Acceptability (CoLa) in the GLUE benchmark dataset. CoLa spawned a thread that led to the development of several similar datasets in different languages, broadening the investigation possibilities to many languages other than English. In this study, leveraging the Italian Corpus of Linguistic Acceptability (ItaCoLA), comprising nearly 10,000 sentences with acceptability judgments, we propose a new methodology that utilizes the neural language model ELECTRA. This approach exceeds the scores obtained from current baselines and demonstrates that it can overcome language-specific limitations in dealing with specific phenomena.

Raising the Bar on Acceptability Judgments Classification: An Experiment on ItaCoLA Using ELECTRA

Guarasci R.
Primo
Writing – Original Draft Preparation
;
Minutolo A.
Secondo
Software
;
Buonaiuto G.
Penultimo
Methodology
;
De Pietro G.;Esposito M.
Ultimo
Project Administration
2024

Abstract

The task of automatically evaluating acceptability judgments has relished increasing success in Natural Language Processing, starting from including the Corpus of Linguistic Acceptability (CoLa) in the GLUE benchmark dataset. CoLa spawned a thread that led to the development of several similar datasets in different languages, broadening the investigation possibilities to many languages other than English. In this study, leveraging the Italian Corpus of Linguistic Acceptability (ItaCoLA), comprising nearly 10,000 sentences with acceptability judgments, we propose a new methodology that utilizes the neural language model ELECTRA. This approach exceeds the scores obtained from current baselines and demonstrates that it can overcome language-specific limitations in dealing with specific phenomena.
2024
Istituto di Calcolo e Reti ad Alte Prestazioni - ICAR
acceptability judgments
BERT
ELECTRA
low-resource languages
natural language processing
sentence classification
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/505682
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