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        <title>Late Fusion of Transformers for Sentiment Analysis of Code-Switched Data</title>
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    <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.</abstract>
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    <identifier type="doi">10.18653/v1/2023.findings-emnlp.430</identifier>
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%0 Conference Proceedings
%T Late Fusion of Transformers for Sentiment Analysis of Code-Switched Data
%A Sharma, Gagan
%A Chinmay, R.
%A Sharma, Raksha
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F sharma-etal-2023-late
%X 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.
%R 10.18653/v1/2023.findings-emnlp.430
%U https://aclanthology.org/2023.findings-emnlp.430
%U https://doi.org/10.18653/v1/2023.findings-emnlp.430
%P 6485-6490

Markdown (Informal)

[Late Fusion of Transformers for Sentiment Analysis of Code-Switched Data](https://aclanthology.org/2023.findings-emnlp.430) (Sharma et al., Findings 2023)

• Late Fusion of Transformers for Sentiment Analysis of Code-Switched Data (Sharma et al., Findings 2023)

ACL

• Gagan Sharma, R Chinmay, and Raksha Sharma. 2023. Late Fusion of Transformers for Sentiment Analysis of Code-Switched Data. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 6485–6490, Singapore. Association for Computational Linguistics.