@inproceedings{shi-etal-2021-cross,
    title = "Cross-Lingual Training of Dense Retrievers for Document Retrieval",
    author = "Shi, Peng  and
      Zhang, Rui  and
      Bai, He  and
      Lin, Jimmy",
    editor = "Ataman, Duygu  and
      Birch, Alexandra  and
      Conneau, Alexis  and
      Firat, Orhan  and
      Ruder, Sebastian  and
      Sahin, Gozde Gul",
    booktitle = "Proceedings of the 1st Workshop on Multilingual Representation Learning",
    month = nov,
    year = "2021",
    address = "Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2021.mrl-1.24/",
    doi = "10.18653/v1/2021.mrl-1.24",
    pages = "251--253",
    abstract = "Dense retrieval has shown great success for passage ranking in English. However, its effectiveness for non-English languages remains unexplored due to limitation in training resources. In this work, we explore different transfer techniques for document ranking from English annotations to non-English languages. Our experiments reveal that zero-shot model-based transfer using mBERT improves search quality. We find that weakly-supervised target language transfer is competitive compared to generation-based target language transfer, which requires translation models."
}