@inproceedings{mao-etal-2019-end,
    title = "End-to-End Sequential Metaphor Identification Inspired by Linguistic Theories",
    author = "Mao, Rui  and
      Lin, Chenghua  and
      Guerin, Frank",
    editor = "Korhonen, Anna  and
      Traum, David  and
      M{\`a}rquez, Llu{\'i}s",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/P19-1378/",
    doi = "10.18653/v1/P19-1378",
    pages = "3888--3898",
    abstract = "End-to-end training with Deep Neural Networks (DNN) is a currently popular method for metaphor identification. However, standard sequence tagging models do not explicitly take advantage of linguistic theories of metaphor identification. We experiment with two DNN models which are inspired by two human metaphor identification procedures. By testing on three public datasets, we find that our models achieve state-of-the-art performance in end-to-end metaphor identification."
}