Evaluating Word Embeddings for Indonesian–English Code-Mixed Text Based on Synthetic Data

Arra’Di Nur Rizal, Sara Stymne


Abstract
Code-mixed texts are abundant, especially in social media, and poses a problem for NLP tools, which are typically trained on monolingual corpora. In this paper, we explore and evaluate different types of word embeddings for Indonesian–English code-mixed text. We propose the use of code-mixed embeddings, i.e. embeddings trained on code-mixed text. Because large corpora of code-mixed text are required to train embeddings, we describe a method for synthesizing a code-mixed corpus, grounded in literature and a survey. Using sentiment analysis as a case study, we show that code-mixed embeddings trained on synthesized data are at least as good as cross-lingual embeddings and better than monolingual embeddings.
Anthology ID:
2020.calcs-1.4
Volume:
Proceedings of the The 4th Workshop on Computational Approaches to Code Switching
Month:
May
Year:
2020
Address:
Marseille, France
Venues:
CALCS | LREC | WS
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
26–35
Language:
English
URL:
https://www.aclweb.org/anthology/2020.calcs-1.4
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.calcs-1.4.pdf