Late Fusion of Transformers for Sentiment Analysis of Code-Switched Data
Gagan Sharma, R Chinmay, Raksha Sharma
Abstract
Code-switching is a common phenomenon in multilingual communities and is often used on social media. However, sentiment analysis of code-switched data is a challenging yet less explored area of research. This paper aims to develop a sentiment analysis system for code-switched data. In this paper, we present a novel approach combining two transformers using logits of their output and feeding them to a neural network for classification. We show the efficacy of our approach using two benchmark datasets, viz., English-Hindi (En-Hi), and English-Spanish (En-Es) availed by Microsoft GLUECoS. Our approach results in an F1 score of 73.66% for En-Es and 61.24% for En-Hi, significantly higher than the best model reported for the GLUECoS benchmark dataset.
BibTeX
@inproceedings{
sharma2023late,
title={Late Fusion of Transformers for Sentiment Analysis of Code-Switched Data},
author={Gagan Sharma and R Chinmay and Raksha Sharma},
booktitle={The 2023 Conference on Empirical Methods in Natural Language Processing},
year={2023},
url={https://openreview.net/forum?id=cgmlfA1sPl}
}