@inproceedings{conia-navigli-2020-conception,
    title = "Conception: Multilingually-Enhanced, Human-Readable Concept Vector Representations",
    author = "Conia, Simone  and
      Navigli, Roberto",
    editor = "Scott, Donia  and
      Bel, Nuria  and
      Zong, Chengqing",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2020.coling-main.291/",
    doi = "10.18653/v1/2020.coling-main.291",
    pages = "3268--3284",
    abstract = "To date, the most successful word, word sense, and concept modelling techniques have used large corpora and knowledge resources to produce dense vector representations that capture semantic similarities in a relatively low-dimensional space. Most current approaches, however, suffer from a monolingual bias, with their strength depending on the amount of data available across languages. In this paper we address this issue and propose Conception, a novel technique for building language-independent vector representations of concepts which places multilinguality at its core while retaining explicit relationships between concepts. Our approach results in high-coverage representations that outperform the state of the art in multilingual and cross-lingual Semantic Word Similarity and Word Sense Disambiguation, proving particularly robust on low-resource languages. Conception {--} its software and the complete set of representations {--} is available at \url{https://github.com/SapienzaNLP/conception}."
}