@inproceedings{sharma-etal-2023-late,
    title = "Late Fusion of Transformers for Sentiment Analysis of Code-Switched Data",
    author = "Sharma, Gagan  and
      Chinmay, R  and
      Sharma, Raksha",
    editor = "Bouamor, Houda  and
      Pino, Juan  and
      Bali, Kalika",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest_wac_2008/2023.findings-emnlp.430/",
    doi = "10.18653/v1/2023.findings-emnlp.430",
    pages = "6485--6490",
    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."
}