@inproceedings{hakimi-parizi-cook-2020-joint,
    title = "Joint Training for Learning Cross-lingual Embeddings with Sub-word Information without Parallel Corpora",
    author = "Hakimi Parizi, Ali  and
      Cook, Paul",
    editor = "Gurevych, Iryna  and
      Apidianaki, Marianna  and
      Faruqui, Manaal",
    booktitle = "Proceedings of the Ninth Joint Conference on Lexical and Computational Semantics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2020.starsem-1.5/",
    pages = "39--49",
    abstract = "In this paper, we propose a novel method for learning cross-lingual word embeddings, that incorporates sub-word information during training, and is able to learn high-quality embeddings from modest amounts of monolingual data and a bilingual lexicon. This method could be particularly well-suited to learning cross-lingual embeddings for lower-resource, morphologically-rich languages, enabling knowledge to be transferred from rich- to lower-resource languages. We evaluate our proposed approach simulating lower-resource languages for bilingual lexicon induction, monolingual word similarity, and document classification. Our results indicate that incorporating sub-word information indeed leads to improvements, and in the case of document classification, performance better than, or on par with, strong benchmark approaches."
}