Multilingual Multiword Expression Identification Using Lateral Inhibition and Domain Adaptation

Andrei-Marius Avram (), Verginica Barbu Mititelu, Vasile Păiș, Dumitru-Clementin Cercel () and Ștefan Trăușan-Matu
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Andrei-Marius Avram: Computer Science and Engineering Department, Faculty of Automatic Control and Computers, University Politehnica of Bucharest, 060042 Bucharest, Romania
Verginica Barbu Mititelu: Research Institute for Artificial Intelligence “Mihai Drăgănescu”, Romanian Academy, 050711 Bucharest, Romania
Vasile Păiș: Research Institute for Artificial Intelligence “Mihai Drăgănescu”, Romanian Academy, 050711 Bucharest, Romania
Dumitru-Clementin Cercel: Computer Science and Engineering Department, Faculty of Automatic Control and Computers, University Politehnica of Bucharest, 060042 Bucharest, Romania
Ștefan Trăușan-Matu: Computer Science and Engineering Department, Faculty of Automatic Control and Computers, University Politehnica of Bucharest, 060042 Bucharest, Romania

Mathematics, 2023, vol. 11, issue 11, 1-18

Abstract: Correctly identifying multiword expressions (MWEs) is an important task for most natural language processing systems since their misidentification can result in ambiguity and misunderstanding of the underlying text. In this work, we evaluate the performance of the mBERT model for MWE identification in a multilingual context by training it on all 14 languages available in version 1.2 of the PARSEME corpus. We also incorporate lateral inhibition and language adversarial training into our methodology to create language-independent embeddings and improve its capabilities in identifying multiword expressions. The evaluation of our models shows that the approach employed in this work achieves better results compared to the best system of the PARSEME 1.2 competition, MTLB-STRUCT, on 11 out of 14 languages for global MWE identification and on 12 out of 14 languages for unseen MWE identification. Additionally, averaged across all languages, our best approach outperforms the MTLB-STRUCT system by 1.23% on global MWE identification and by 4.73% on unseen global MWE identification.

Keywords: multiword expression identification; multilingual; lateral inhibition; domain adaptation; PARSEME corpus (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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