@inproceedings{hakimi-parizi-cook-2018-character,
    title = "Do Character-Level Neural Network Language Models Capture Knowledge of Multiword Expression Compositionality?",
    author = "Hakimi Parizi, Ali  and
      Cook, Paul",
    editor = "Savary, Agata  and
      Ramisch, Carlos  and
      Hwang, Jena D.  and
      Schneider, Nathan  and
      Andresen, Melanie  and
      Pradhan, Sameer  and
      Petruck, Miriam R. L.",
    booktitle = "Proceedings of the Joint Workshop on Linguistic Annotation, Multiword Expressions and Constructions ({LAW}-{MWE}-{C}x{G}-2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/iwcs-25-ingestion/W18-4920/",
    pages = "185--192",
    abstract = "In this paper, we propose the first model for multiword expression (MWE) compositionality prediction based on character-level neural network language models. Experimental results on two kinds of MWEs (noun compounds and verb-particle constructions) and two languages (English and German) suggest that character-level neural network language models capture knowledge of multiword expression compositionality, in particular for English noun compounds and the particle component of English verb-particle constructions. In contrast to many other approaches to MWE compositionality prediction, this character-level approach does not require token-level identification of MWEs in a training corpus, and can potentially predict the compositionality of out-of-vocabulary MWEs."
}