The use of contextualised word embeddings allowed for a relevant performance increase for almost all Natural Language Processing (NLP) applications. Recently some new models especially developed for Italian became available to scholars. This work aims at applying simple fine-tuning methods for producing high-performance solutions at the EVALITA KIPOS PoS-tagging task (Bosco et al., 2020).

UniBO@KIPoS: Fine-tuning the Italian “BERTology” for PoS-tagging Spoken Data

Tamburini Fabio
Primo
2020

Abstract

The use of contextualised word embeddings allowed for a relevant performance increase for almost all Natural Language Processing (NLP) applications. Recently some new models especially developed for Italian became available to scholars. This work aims at applying simple fine-tuning methods for producing high-performance solutions at the EVALITA KIPOS PoS-tagging task (Bosco et al., 2020).
Proceedings of the Seventh Evaluation Campaign of Natural Language Processing and Speech Tools for Italian. Final Workshop (EVALITA 2020)
497
500
Tamburini Fabio
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11585/802196
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