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.- Anthology ID:
- 2023.findings-emnlp.430
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2023
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Houda Bouamor, Juan Pino, Kalika Bali
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 6485–6490
- Language:
- URL:
- https://aclanthology.org/2023.findings-emnlp.430
- DOI:
- 10.18653/v1/2023.findings-emnlp.430
- Cite (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.
- Cite (Informal):
- Late Fusion of Transformers for Sentiment Analysis of Code-Switched Data (Sharma et al., Findings 2023)
- PDF:
- https://preview.aclanthology.org/add_acl24_videos/2023.findings-emnlp.430.pdf