Assessing BERT’s ability to learn Italian syntax: a study on null-subject and agreement phenomena
The work presented in this paper investigates the ability of BERT neural language model pretrained in Italian to embed syntactic dependency relationships into its layers, by approximating a Dependency Parse Tree. To this end, a structural probe, namely a supervised model able to extract linguistic s...
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| Vydané v: | Journal of ambient intelligence and humanized computing Ročník 14; číslo 1; s. 289 - 303 |
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| Hlavní autori: | , , , , |
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| Jazyk: | English |
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Berlin/Heidelberg
Springer Berlin Heidelberg
01.01.2023
Springer Nature B.V |
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| ISSN: | 1868-5137, 1868-5145 |
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| Abstract | The work presented in this paper investigates the ability of BERT neural language model pretrained in Italian to embed syntactic dependency relationships into its layers, by approximating a Dependency Parse Tree. To this end, a structural probe, namely a supervised model able to extract linguistic structures from a language model, has been trained leveraging the contextual embeddings from the layers of BERT. An experimental assessment has been performed using an Italian version of BERT-base model and a set of datasets for Italian labelled with Universal Dependencies formalism. The results, achieved using standard metrics of dependency parsers, have shown that a knowledge of the Italian syntax is embedded in central-upper layers of the BERT model, according to what observed in literature for the English case. In addition, the probe has been also used to experimentally evaluate the BERT model behaviour in case of two specific syntactic phenomena in Italian, namely null-subject and subject-verb-agreement, showing better performance than an Italian state-of-the-art parser. These findings can open a path for the development of new hybrid approaches, exploiting the probe to integrate or improve limits or weaknesses in analysing articulated constructions of Italian syntax, traditionally complex to be parsed. |
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| AbstractList | The work presented in this paper investigates the ability of BERT neural language model pretrained in Italian to embed syntactic dependency relationships into its layers, by approximating a Dependency Parse Tree. To this end, a structural probe, namely a supervised model able to extract linguistic structures from a language model, has been trained leveraging the contextual embeddings from the layers of BERT. An experimental assessment has been performed using an Italian version of BERT-base model and a set of datasets for Italian labelled with Universal Dependencies formalism. The results, achieved using standard metrics of dependency parsers, have shown that a knowledge of the Italian syntax is embedded in central-upper layers of the BERT model, according to what observed in literature for the English case. In addition, the probe has been also used to experimentally evaluate the BERT model behaviour in case of two specific syntactic phenomena in Italian, namely null-subject and subject-verb-agreement, showing better performance than an Italian state-of-the-art parser. These findings can open a path for the development of new hybrid approaches, exploiting the probe to integrate or improve limits or weaknesses in analysing articulated constructions of Italian syntax, traditionally complex to be parsed. |
| Author | Silvestri, Stefano Guarasci, Raffaele Fujita, Hamido De Pietro, Giuseppe Esposito, Massimo |
| Author_xml | – sequence: 1 givenname: Raffaele surname: Guarasci fullname: Guarasci, Raffaele organization: National Research Council of Italy, Institute for High Performance Computing and Networking (ICAR-CNR) – sequence: 2 givenname: Stefano orcidid: 0000-0002-9890-8409 surname: Silvestri fullname: Silvestri, Stefano email: stefano.silvestri@icar.cnr.it organization: National Research Council of Italy, Institute for High Performance Computing and Networking (ICAR-CNR) – sequence: 3 givenname: Giuseppe surname: De Pietro fullname: De Pietro, Giuseppe organization: National Research Council of Italy, Institute for High Performance Computing and Networking (ICAR-CNR) – sequence: 4 givenname: Hamido surname: Fujita fullname: Fujita, Hamido organization: Faculty of Information Technology, Ho Chi Minh City University of Technology (HUTECH), Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada, Faculty of Software and Information Science, Iwate Prefectural University – sequence: 5 givenname: Massimo surname: Esposito fullname: Esposito, Massimo organization: National Research Council of Italy, Institute for High Performance Computing and Networking (ICAR-CNR) |
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| Keywords | Neural language model Structural probe Syntactic phenomena Dependency parse tree |
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| SubjectTerms | Artificial Intelligence Computational Intelligence Engineering English language Italian language Knowledge Linguistics Machine translation Morphology Natural language Original Research Parsers Robotics and Automation Semantics Syntax User Interfaces and Human Computer Interaction |
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| Title | Assessing BERT’s ability to learn Italian syntax: a study on null-subject and agreement phenomena |
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