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arXiv · 2009.09879

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

Ahmed Sultan·Mahmoud Salim·Amina Gaber·Islam El Hosary
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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".

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Ahmed Sultan, Mahmoud Salim, Amina Gaber, Islam El Hosary. 2020-09-21. WESSA at SemEval-2020 Task 9: Code-Mixed Sentiment Analysis using Transformers. https://arxiv.org/abs/2009.09879

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