@inproceedings{ma-etal-2020-xlp,
    title = "{XLP} at {S}em{E}val-2020 Task 9: Cross-lingual Models with Focal Loss for Sentiment Analysis of Code-Mixing Language",
    author = "Ma, Yili  and
      Zhao, Liang  and
      Hao, Jie",
    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-1.126/",
    doi = "10.18653/v1/2020.semeval-1.126",
    pages = "975--980",
    abstract = "In this paper, we present an approach for sentiment analysis in code-mixed language on twitter defined in SemEval-2020 Task 9. Our team (referred as LiangZhao) employ different multilingual models with weighted loss focused on complexity of code-mixing in sentence, in which the best model achieved f1-score of 0.806 and ranked 1st of subtask- Sentimix Spanglish. The performance of method is analyzed and each component of our architecture is demonstrated."
}