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

2020/09/21 by Ahmed Sultan, Mahmoud Salim, Sultan, Ahmed +5 · 1 citation
Computer Science · #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #FOS: Computer and information sciences #Natural Language Processing Techniques #Text Readability and Simplification #Topic Modeling

paper · pdf · doi:10.48550/arxiv.2009.09879

openalex publication_date 2020/09/21 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28

Abstract

In this paper, we describe our system submitted for SemEval 2020 Task 9,\nSentiment Analysis for Code-Mixed Social Media Text alongside other\nexperiments. Our best performing system is a Transfer Learning-based model that\nfine-tunes "XLM-RoBERTa", a transformer-based multilingual masked language\nmodel, on monolingual English and Spanish data and Spanish-English code-mixed\ndata. Our system outperforms the official task baseline by achieving a 70.1%\naverage F1-Score on the official leaderboard using the test set. For later\nsubmissions, our system manages to achieve a 75.9% average F1-Score on the test\nset using CodaLab username "ahmed0sultan".\n

Cited by

• Beyond Monolingual Assumptions: A Survey of Code-Switched NLP in the Era of Large Language Models

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