WESSA at SemEval-2020 Task 9: Code-Mixed Sentiment Analysis Using Transformers

Ahmed Sultan, Mahmoud Salim, Amina Gaber, Islam El Hosary


Abstract
In this paper, we describe our system submitted for SemEval 2020 Task 9, Sentiment Analysis for Code-Mixed Social Media Text alongside other experiments. Our best performing system is a Transfer Learning-based model that fine-tunes XLM-RoBERTa, a transformer-based multilingual masked language model, on monolingual English and Spanish data and Spanish-English code-mixed data. Our system outperforms the official task baseline by achieving a 70.1% average F1-Score on the official leaderboard using the test set. For later submissions, our system manages to achieve a 75.9% average F1-Score on the test set using CodaLab username “ahmed0sultan”.
Anthology ID:
2020.semeval-1.181
Volume:
Proceedings of the Fourteenth Workshop on Semantic Evaluation
Month:
December
Year:
2020
Address:
Barcelona (online)
Editors:
Aurelie Herbelot, Xiaodan Zhu, Alexis Palmer, Nathan Schneider, Jonathan May, Ekaterina Shutova
Venue:
SemEval
SIG:
SIGLEX
Publisher:
International Committee for Computational Linguistics
Note:
Pages:
1342–1347
Language:
URL:
https://aclanthology.org/2020.semeval-1.181
DOI:
10.18653/v1/2020.semeval-1.181
Bibkey:
Cite (ACL):
Ahmed Sultan, Mahmoud Salim, Amina Gaber, and Islam El Hosary. 2020. WESSA at SemEval-2020 Task 9: Code-Mixed Sentiment Analysis Using Transformers. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 1342–1347, Barcelona (online). International Committee for Computational Linguistics.
Cite (Informal):
WESSA at SemEval-2020 Task 9: Code-Mixed Sentiment Analysis Using Transformers (Sultan et al., SemEval 2020)
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PDF:
https://aclanthology.org/2020.semeval-1.181.pdf