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        <title>mAggretriever: A Simple yet Effective Approach to Zero-Shot Multilingual Dense Retrieval</title>
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    <abstract>Multilingual information retrieval (MLIR) is a crucial yet challenging task due to the need for human annotations in multiple languages, making training data creation labor-intensive. In this paper, we introduce mAggretriever, which effectively leverages semantic and lexical features from pre-trained multilingual transformers (e.g., mBERT and XLM-R) for dense retrieval. To enhance training and inference efficiency, we employ approximate masked-language modeling prediction for computing lexical features, reducing 70–85% GPU memory requirement for mAggretriever fine-tuning. Empirical results demonstrate that mAggretriever, fine-tuned solely on English training data, surpasses existing state-of-the-art multilingual dense retrieval models that undergo further training on large-scale MLIR training data. Our code is available at url.</abstract>
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%0 Conference Proceedings
%T mAggretriever: A Simple yet Effective Approach to Zero-Shot Multilingual Dense Retrieval
%A Lin, Sheng-Chieh
%A Ahmad, Amin
%A Lin, Jimmy
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F lin-etal-2023-maggretriever
%X Multilingual information retrieval (MLIR) is a crucial yet challenging task due to the need for human annotations in multiple languages, making training data creation labor-intensive. In this paper, we introduce mAggretriever, which effectively leverages semantic and lexical features from pre-trained multilingual transformers (e.g., mBERT and XLM-R) for dense retrieval. To enhance training and inference efficiency, we employ approximate masked-language modeling prediction for computing lexical features, reducing 70–85% GPU memory requirement for mAggretriever fine-tuning. Empirical results demonstrate that mAggretriever, fine-tuned solely on English training data, surpasses existing state-of-the-art multilingual dense retrieval models that undergo further training on large-scale MLIR training data. Our code is available at url.
%R 10.18653/v1/2023.emnlp-main.715
%U https://aclanthology.org/2023.emnlp-main.715
%U https://doi.org/10.18653/v1/2023.emnlp-main.715
%P 11688-11696

Markdown (Informal)

[mAggretriever: A Simple yet Effective Approach to Zero-Shot Multilingual Dense Retrieval](https://aclanthology.org/2023.emnlp-main.715) (Lin et al., EMNLP 2023)

• mAggretriever: A Simple yet Effective Approach to Zero-Shot Multilingual Dense Retrieval (Lin et al., EMNLP 2023)

ACL

• Sheng-Chieh Lin, Amin Ahmad, and Jimmy Lin. 2023. mAggretriever: A Simple yet Effective Approach to Zero-Shot Multilingual Dense Retrieval. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 11688–11696, Singapore. Association for Computational Linguistics.