@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://aclanthology.org/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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Cross-Lingual Training of Dense Retrievers for Document Retrieval
%A Shi, Peng
%A Zhang, Rui
%A Bai, He
%A Lin, Jimmy
%Y Ataman, Duygu
%Y Birch, Alexandra
%Y Conneau, Alexis
%Y Firat, Orhan
%Y Ruder, Sebastian
%Y Sahin, Gozde Gul
%S Proceedings of the 1st Workshop on Multilingual Representation Learning
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F shi-etal-2021-cross
%X 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.
%R 10.18653/v1/2021.mrl-1.24
%U https://aclanthology.org/2021.mrl-1.24
%U https://doi.org/10.18653/v1/2021.mrl-1.24
%P 251-253
Markdown (Informal)
[Cross-Lingual Training of Dense Retrievers for Document Retrieval](https://aclanthology.org/2021.mrl-1.24) (Shi et al., MRL 2021)
ACL