@inproceedings{skurniak-etal-2018-multi,
    title = "Multi-Module Recurrent Neural Networks with Transfer Learning",
    author = "Skurniak, Filip  and
      Janicka, Maria  and
      Wawer, Aleksander",
    editor = "Beigman Klebanov, Beata  and
      Shutova, Ekaterina  and
      Lichtenstein, Patricia  and
      Muresan, Smaranda  and
      Wee, Chee",
    booktitle = "Proceedings of the Workshop on Figurative Language Processing",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/W18-0917/",
    doi = "10.18653/v1/W18-0917",
    pages = "128--132",
    abstract = "This paper describes multiple solutions designed and tested for the problem of word-level metaphor detection. The proposed systems are all based on variants of recurrent neural network architectures. Specifically, we explore multiple sources of information: pre-trained word embeddings (Glove), a dictionary of language concreteness and a transfer learning scenario based on the states of an encoder network from neural network machine translation system. One of the architectures is based on combining all three systems: (1) Neural CRF (Conditional Random Fields), trained directly on the metaphor data set; (2) Neural Machine Translation encoder of a transfer learning scenario; (3) a neural network used to predict final labels, trained directly on the metaphor data set. Our results vary between test sets: Neural CRF standalone is the best one on submission data, while combined system scores the highest on a test subset randomly selected from training data."
}