@inproceedings{hakimi-parizi-cook-2021-evaluating,
    title = "Evaluating a Joint Training Approach for Learning Cross-lingual Embeddings with Sub-word Information without Parallel Corpora on Lower-resource Languages",
    author = "Hakimi Parizi, Ali  and
      Cook, Paul",
    editor = "Ku, Lun-Wei  and
      Nastase, Vivi  and
      Vuli{\'c}, Ivan",
    booktitle = "Proceedings of *SEM 2021: The Tenth Joint Conference on Lexical and Computational Semantics",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2021.starsem-1.29/",
    doi = "10.18653/v1/2021.starsem-1.29",
    pages = "302--307",
    abstract = "Cross-lingual word embeddings provide a way for information to be transferred between languages. In this paper we evaluate an extension of a joint training approach to learning cross-lingual embeddings that incorporates sub-word information during training. This method could be particularly well-suited to lower-resource and morphologically-rich languages because it can be trained on modest size monolingual corpora, and is able to represent out-of-vocabulary words (OOVs). We consider bilingual lexicon induction, including an evaluation focused on OOVs. We find that this method achieves improvements over previous approaches, particularly for OOVs."
}