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

Dana-Maria Iliescu, Rasmus Grand, Sara Qirko, Rob van der Goot


Abstract
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%.
Anthology ID:
2021.calcs-1.9
Volume:
Proceedings of the Fifth Workshop on Computational Approaches to Linguistic Code-Switching
Month:
June
Year:
2021
Address:
Online
Venues:
CALCS | NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
65–71
Language:
URL:
https://aclanthology.org/2021.calcs-1.9
DOI:
10.18653/v1/2021.calcs-1.9
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.calcs-1.9.pdf
Data
LinCE