@InProceedings{rei-gerz-vuli:2018:Short,
  author    = {Rei, Marek  and  Gerz, Daniela  and  Vulić, Ivan},
  title     = {Scoring Lexical Entailment with a Supervised Directional Similarity Network},
  booktitle = {Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},
  month     = {July},
  year      = {2018},
  address   = {Melbourne, Australia},
  publisher = {Association for Computational Linguistics},
  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%.},
  url       = {http://www.aclweb.org/anthology/P18-2101}
}