@inproceedings{shi-etal-2020-cross,
    title = "Cross-Lingual Training of Neural Models for Document Ranking",
    author = "Shi, Peng  and
      Bai, He  and
      Lin, Jimmy",
    editor = "Cohn, Trevor  and
      He, Yulan  and
      Liu, Yang",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2020.findings-emnlp.249/",
    doi = "10.18653/v1/2020.findings-emnlp.249",
    pages = "2768--2773",
    abstract = "We tackle the challenge of cross-lingual training of neural document ranking models for mono-lingual retrieval, specifically leveraging relevance judgments in English to improve search in non-English languages. Our work successfully applies multi-lingual BERT (mBERT) to document ranking and additionally compares against a number of alternatives: translating the training data, translating documents, multi-stage hybrids, and ensembles. Experiments on test collections in six different languages from diverse language families reveal many interesting findings: model-based relevance transfer using mBERT can significantly improve search quality in (non-English) mono-lingual retrieval, but other {\textquotedblleft}low resource{\textquotedblright} approaches are competitive as well."
}