@inproceedings{sultan-etal-2020-wessa,
    title = "{WESSA} at {S}em{E}val-2020 Task 9: Code-Mixed Sentiment Analysis Using Transformers",
    author = "Sultan, Ahmed  and
      Salim, Mahmoud  and
      Gaber, Amina  and
      El Hosary, Islam",
    editor = "Herbelot, Aurelie  and
      Zhu, Xiaodan  and
      Palmer, Alexis  and
      Schneider, Nathan  and
      May, Jonathan  and
      Shutova, Ekaterina",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2020.semeval-1.181/",
    doi = "10.18653/v1/2020.semeval-1.181",
    pages = "1342--1347",
    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 {\textquotedblleft}ahmed0sultan{\textquotedblright}."
}