@inproceedings{bojkovsky-pikuliak-2019-stufiit,
    title = "{STUFIIT} at {S}em{E}val-2019 Task 5: Multilingual Hate Speech Detection on {T}witter with {MUSE} and {ELM}o Embeddings",
    author = "Bojkovsk{\'y}, Michal  and
      Pikuliak, Mat{\'u}{\v{s}}",
    editor = "May, Jonathan  and
      Shutova, Ekaterina  and
      Herbelot, Aurelie  and
      Zhu, Xiaodan  and
      Apidianaki, Marianna  and
      Mohammad, Saif M.",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/S19-2082/",
    doi = "10.18653/v1/S19-2082",
    pages = "464--468",
    abstract = "We present a number of models used for hate speech detection for Semeval 2019 Task-5: Hateval. We evaluate the viability of multilingual learning for this task. We also experiment with adversarial learning as a means of creating a multilingual model. Ultimately our multilingual models have had worse results than their monolignual counterparts. We find that the choice of word representations (word embeddings) is very crucial for deep learning as a simple switch between MUSE and ELMo embeddings has shown a 3-4{\%} increase in accuracy. This also shows the importance of context when dealing with online content."
}