@inproceedings{breitwieser-2022-contextualizing,
    title = "Can Contextualizing User Embeddings Improve Sarcasm and Hate Speech Detection?",
    author = "Breitwieser, Kim",
    editor = "Bamman, David  and
      Hovy, Dirk  and
      Jurgens, David  and
      Keith, Katherine  and
      O'Connor, Brendan  and
      Volkova, Svitlana",
    booktitle = "Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+CSS)",
    month = nov,
    year = "2022",
    address = "Abu Dhabi, UAE",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2022.nlpcss-1.14/",
    doi = "10.18653/v1/2022.nlpcss-1.14",
    pages = "126--139",
    abstract = "While implicit embeddings so far have been mostly concerned with creating an overall representation of the user, we evaluate a different approach. By only considering content directed at a specific topic, we create sub-user embeddings, and measure their usefulness on the tasks of sarcasm and hate speech detection. In doing so, we show that task-related topics can have a noticeable effect on model performance, especially when dealing with intended expressions like sarcasm, but less so for hate speech, which is usually labelled as such on the receiving end."
}