@inproceedings{ngomane-etal-2023-unsupervised,
    title = "Unsupervised Cross-lingual Word Embedding Representation for {E}nglish-isi{Z}ulu",
    author = "Ngomane, Derwin  and
      Mabuya, Rooweither  and
      Abbott, Jade  and
      Marivate, Vukosi",
    editor = "Mabuya, Rooweither  and
      Mthobela, Don  and
      Setaka, Mmasibidi  and
      Van Zaanen, Menno",
    booktitle = "Proceedings of the Fourth workshop on Resources for African Indigenous Languages (RAIL 2023)",
    month = may,
    year = "2023",
    address = "Dubrovnik, Croatia",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.rail-1.2",
    doi = "10.18653/v1/2023.rail-1.2",
    pages = "11--17",
    abstract = "In this study, we investigate the effectiveness of using cross-lingual word embeddings for zero-shot transfer learning between a language with an abundant resource, English, and a languagewith limited resource, isiZulu. IsiZulu is a part of the South African Nguni language family, which is characterised by complex agglutinating morphology. We use VecMap, an open source tool, to obtain cross-lingual word embeddings. To perform an extrinsic evaluation of the effectiveness of the embeddings, we train a news classifier on labelled English data in order to categorise unlabelled isiZulu data using zero-shot transfer learning. In our study, we found our model to have a weighted average F1-score of 0.34. Our findings demonstrate that VecMap generates modular word embeddings in the cross-lingual space that have an impact on the downstream classifier used for zero-shot transfer learning.",
}