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

• URL: http://arxiv.org/abs/2009.09879v1
• Date: Mon, 21 Sep 2020 13:59:24 GMT
• Title: WESSA at SemEval-2020 Task 9: Code-Mixed Sentiment Analysis using Transformers
• Authors: Ahmed Sultan (WideBot), Mahmoud Salim (WideBot), Amina Gaber (WideBot), Islam El Hosary (WideBot)
• Abstract summary: We describe our system submitted for SemEval 2020 Task 9, Sentiment Analysis for Code-Mixed Social Media Text. Our best performing system is a Transfer Learning-based model that fine-tunes "XLM-RoBERTa" For later submissions, our system manages to achieve a 75.9% average F1-Score on the test set using CodaLab username "ahmed0sultan"
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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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