@inproceedings{benballa-etal-2019-saagie,
    title = "Saagie at {S}emeval-2019 Task 5: From Universal Text Embeddings and Classical Features to Domain-specific Text Classification",
    author = "Benballa, Miriam  and
      Collet, Sebastien  and
      Picot-Clemente, Romain",
    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/iwcs-25-ingestion/S19-2083/",
    doi = "10.18653/v1/S19-2083",
    pages = "469--475",
    abstract = "This paper describes our contribution to SemEval 2019 Task 5: Hateval. We propose to investigate how domain-specific text classification task can benefit from pretrained state of the art language models and how they can be combined with classical handcrafted features. For this purpose, we propose an approach based on a feature-level Meta-Embedding to let the model choose which features to keep and how to use them."
}