@inproceedings{rei-etal-2018-scoring,
    title = "Scoring Lexical Entailment with a Supervised Directional Similarity Network",
    author = "Rei, Marek  and
      Gerz, Daniela  and
      Vuli{\'c}, Ivan",
    editor = "Gurevych, Iryna  and
      Miyao, Yusuke",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/P18-2101/",
    doi = "10.18653/v1/P18-2101",
    pages = "638--643",
    abstract = "We present the Supervised Directional Similarity Network, a novel neural architecture for learning task-specific transformation functions on top of general-purpose word embeddings. Relying on only a limited amount of supervision from task-specific scores on a subset of the vocabulary, our architecture is able to generalise and transform a general-purpose distributional vector space to model the relation of lexical entailment. Experiments show excellent performance on scoring graded lexical entailment, raising the state-of-the-art on the HyperLex dataset by approximately 25{\%}."
}