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        <title>Cross-Lingual Training of Neural Models for Document Ranking</title>
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    <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 “low resource” approaches are competitive as well.</abstract>
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    <identifier type="doi">10.18653/v1/2020.findings-emnlp.249</identifier>
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%0 Conference Proceedings
%T Cross-Lingual Training of Neural Models for Document Ranking
%A Shi, Peng
%A Bai, He
%A Lin, Jimmy
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Findings of the Association for Computational Linguistics: EMNLP 2020
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F shi-etal-2020-cross
%X 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 “low resource” approaches are competitive as well.
%R 10.18653/v1/2020.findings-emnlp.249
%U https://aclanthology.org/2020.findings-emnlp.249
%U https://doi.org/10.18653/v1/2020.findings-emnlp.249
%P 2768-2773

Markdown (Informal)

[Cross-Lingual Training of Neural Models for Document Ranking](https://aclanthology.org/2020.findings-emnlp.249) (Shi et al., Findings 2020)

• Cross-Lingual Training of Neural Models for Document Ranking (Shi et al., Findings 2020)

ACL

• Peng Shi, He Bai, and Jimmy Lin. 2020. Cross-Lingual Training of Neural Models for Document Ranking. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 2768–2773, Online. Association for Computational Linguistics.