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).
Tamburini Fabio (2020). UniBO@KIPoS: Fine-tuning the Italian “BERTology” for PoS-tagging Spoken Data. Aachen : CEUR-WS.
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).File in questo prodotto:
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Tamburini_KIPoS.pdf
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