Code-Mixed Probes Show How Pre-Trained Models Generalise On Code-Switched Text

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3A9JDIQJMN" target="_blank" >RIV/00216208:11320/25:9JDIQJMN - isvavai.cz</a>

Result on the web

<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195953168&partnerID=40&md5=c602babfbaae94e522f4cc9eae715e5c" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195953168&partnerID=40&md5=c602babfbaae94e522f4cc9eae715e5c</a>

DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Code-Mixed Probes Show How Pre-Trained Models Generalise On Code-Switched Text

Original language description

Code-switching is a prevalent linguistic phenomenon in which multilingual individuals seamlessly alternate between languages. Despite its widespread use online and recent research trends in this area, research in code-switching presents unique challenges, primarily stemming from the scarcity of labelled data and available resources. In this study we investigate how pre-trained Language Models handle code-switched text in three dimensions: a) the ability of PLMs to detect code-switched text, b) variations in the structural information that PLMs utilise to capture code-switched text, and c) the consistency of semantic information representation in code-switched text. To conduct a systematic and controlled evaluation of the language models in question, we create a novel dataset of well-formed naturalistic code-switched text along with parallel translations into the source languages. Our findings reveal that pre-trained language models are effective in generalising to code-switched text, shedding light on abilities of these models to generalise representations to CS corpora. We release all our code and data, including the novel corpus, at https://github.com/francesita/code-mixed-probes. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

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Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Jt. Int. Conf. Comput. Linguist., Lang. Resour. Eval., LREC-COLING - Main Conf. Proc.

ISBN

978-249381410-4

ISSN

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e-ISSN

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Number of pages

12

Pages from-to

3457-3468

Publisher name

European Language Resources Association (ELRA)

Place of publication

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Event location

Torino, Italia

Event date

Jan 1, 2025

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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