@inproceedings{brooks-youssef-2020-metaphor,
    title = "Metaphor Detection using Ensembles of Bidirectional Recurrent Neural Networks",
    author = "Brooks, Jennifer  and
      Youssef, Abdou",
    editor = "Klebanov, Beata Beigman  and
      Shutova, Ekaterina  and
      Lichtenstein, Patricia  and
      Muresan, Smaranda  and
      Wee, Chee  and
      Feldman, Anna  and
      Ghosh, Debanjan",
    booktitle = "Proceedings of the Second Workshop on Figurative Language Processing",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2020.figlang-1.33/",
    doi = "10.18653/v1/2020.figlang-1.33",
    pages = "244--249",
    abstract = "In this paper we present our results from the Second Shared Task on Metaphor Detection, hosted by the Second Workshop on Figurative Language Processing. We use an ensemble of RNN models with bidirectional LSTMs and bidirectional attention mechanisms. Some of the models were trained on all parts of speech. Each of the other models was trained on one of four categories for parts of speech: {\textquotedblleft}nouns{\textquotedblright}, {\textquotedblleft}verbs{\textquotedblright}, {\textquotedblleft}adverbs/adjectives{\textquotedblright}, or {\textquotedblleft}other{\textquotedblright}. The models were combined into voting pools and the voting pools were combined using the logical {\textquotedblleft}OR{\textquotedblright} operator."
}