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Much Gracias: Semi-supervised Code-switch Detection for Spanish-English: How far can we get?

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Because of globalization, it is becoming more and more common to use multiple languages in a single utterance, also called code-switching. This results in special linguistic structures and, therefore, poses many challenges for Natural Language Processing. Existing models for language identification in code-switched data are all supervised, requiring annotated training data which is only available for a limited number of language pairs. In this paper, we explore semi-supervised approaches, that exploit out-of-domain mono-lingual training data. We experiment with word uni-grams, word n-grams, character n-grams, Viterbi Decoding, Latent Dirichlet Allocation, Support Vector Machine and Logistic Regression. The Viterbi model was the best semi-supervised model, scoring a weighted F1 score of 92.23%, whereas a fully supervised state-of-the-art BERT-based model scored 98.43%.
Original languageEnglish
Title of host publicationProceedings of the Fifth Workshop on Computational Approaches to Linguistic Code-Switching
Number of pages6
PublisherAssociation for Computational Linguistics
Publication dateJun 2021
Pages65
Publication statusPublished - Jun 2021
EventFifth Workshop on Computational Approaches to Linguistic Code-Switching -
Duration: 11 Jun 202111 Jun 2021
Conference number: 5

Conference

ConferenceFifth Workshop on Computational Approaches to Linguistic Code-Switching
Nummer5
Periode11/06/202111/06/2021
SeriesProceedings of the Fifth Workshop on Computational Approaches to Linguistic Code-Switching

ID: 86422442