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
- Venue:
- CALCS
- 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
- Cite (ACL):
- Dana-Maria Iliescu, Rasmus Grand, Sara Qirko, and Rob van der Goot. 2021. Much Gracias: Semi-supervised Code-switch Detection for Spanish-English: How far can we get?. In Proceedings of the Fifth Workshop on Computational Approaches to Linguistic Code-Switching, pages 65–71, Online. Association for Computational Linguistics.
- Cite (Informal):
- Much Gracias: Semi-supervised Code-switch Detection for Spanish-English: How far can we get? (Iliescu et al., CALCS 2021)
- PDF:
- https://preview.aclanthology.org/nodalida-main-page/2021.calcs-1.9.pdf
- Data
- LinCE