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BERT syntactic transfer: A computational experiment on Italian, French and English languages

Articolo
Data di Pubblicazione:
2022
Abstract:
This paper investigates the ability of multilingual BERT (mBERT) language model to transfer syntactic knowledge cross-lingually, verifying if and to which extent syntactic dependency relationships learnt in a language are maintained in other languages. In detail, the main contributions of this paper are: (i) an analysis of the cross-lingual syntactic transfer capability of mBERT model; (ii) a detailed comparison of cross-language syntactic transfer among languages belonging to different branches of the Indo-European languages, namely English, Italian and French, which present very different syntactic constructions; (iii) a study on the transferability of a syntactic phenomenon peculiar of Italian language, namely the pronoun dropping (pro-drop), also known as omissibility of the subject. To this end, a structural probe devoted to reconstruct the dependency parse tree of a sentence has been exploited, representing the input sentences with the contextual embeddings from mBERT layers. The results of the experimental assessment have shown a transfer of syntactic knowledge of the mBERT model among these languages. Moreover, the behaviour of the probe in the transition from pro-drop to non-pro-drop languages and vice versa has proven to be more effective in case of languages sharing a common linguistic matrix. The possibility of transferring syntactical knowledge, especially in the case of specific phenomena, meets both a theoretical need and can have important practical implications in syntactic tasks, such as dependency parsing.
Tipologia CRIS:
01.01 Articolo in rivista
Keywords:
Cross Language; Dependency Parse Tree; Language models; Multilingual BERT; Transfer learning; Syntactic phenomena
Elenco autori:
DE PIETRO, Giuseppe; Esposito, Massimo; Guarasci, Raffaele; Silvestri, Stefano
Autori di Ateneo:
ESPOSITO MASSIMO
GUARASCI RAFFAELE
SILVESTRI STEFANO
Link alla scheda completa:
https://iris.cnr.it/handle/20.500.14243/444247
Pubblicato in:
COMPUTER SPEECH AND LANGUAGE
Journal
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http://www.scopus.com/record/display.url?eid=2-s2.0-85109004988&origin=inward
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