Using task-specific pre-training and leveraging cross-lingual transfer are two of the most popular ways to handle code-switched data. In this paper, we aim to compare the effects of both for the task of sentiment analysis. We work with two Dravidian Code-Switched languages - Tamil-Engish and Malayalam-English and four different BERT based models. We compare the effects of task-specific pre-training and cross-lingual transfer and find that task-specific pre-training results in superior zero-shot and supervised performance when compared to performance achieved by leveraging cross-lingual transfer from multilingual BERT models.
@article{arxiv.2102.12407,
title = {Task-Specific Pre-Training and Cross Lingual Transfer for Code-Switched Data},
author = {Akshat Gupta and Sai Krishna Rallabandi and Alan Black},
journal= {arXiv preprint arXiv:2102.12407},
year = {2021}
}