@inproceedings{glavas-vulic-2019-generalized,
    title = "Generalized Tuning of Distributional Word Vectors for Monolingual and Cross-Lingual Lexical Entailment",
    author = "Glava{\v{s}}, Goran  and
      Vuli{\'c}, Ivan",
    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/fix-sig-urls/P19-1476/",
    doi = "10.18653/v1/P19-1476",
    pages = "4824--4830",
    abstract = "Lexical entailment (LE; also known as hyponymy-hypernymy or is-a relation) is a core asymmetric lexical relation that supports tasks like taxonomy induction and text generation. In this work, we propose a simple and effective method for fine-tuning distributional word vectors for LE. Our Generalized Lexical ENtailment model (GLEN) is decoupled from the word embedding model and applicable to any distributional vector space. Yet {--} unlike existing retrofitting models {--} it captures a general specialization function allowing for LE-tuning of the entire distributional space and not only the vectors of words seen in lexical constraints. Coupled with a multilingual embedding space, GLEN seamlessly enables cross-lingual LE detection. We demonstrate the effectiveness of GLEN in graded LE and report large improvements (over 20{\%} in accuracy) over state-of-the-art in cross-lingual LE detection."
}