@inproceedings{singh-lefever-2020-lt3,
    title = "{LT}3 at {S}em{E}val-2020 Task 9: Cross-lingual Embeddings for Sentiment Analysis of {H}inglish Social Media Text",
    author = "Singh, Pranaydeep  and
      Lefever, Els",
    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/moar-dois/2020.semeval-1.173/",
    doi = "10.18653/v1/2020.semeval-1.173",
    pages = "1288--1293",
    abstract = "This paper describes our contribution to the SemEval-2020 Task 9 on Sentiment Analysis for Code-mixed Social Media Text. We investigated two approaches to solve the task of Hinglish sentiment analysis. The first approach uses cross-lingual embeddings resulting from projecting Hinglish and pre-trained English FastText word embeddings in the same space. The second approach incorporates pre-trained English embeddings that are incrementally retrained with a set of Hinglish tweets. The results show that the second approach performs best, with an F1-score of 70.52{\%} on the held-out test data."
}