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        <title>Noisy Self-Training with Synthetic Queries for Dense Retrieval</title>
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            <namePart type="given">Kalika</namePart>
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    <abstract>Although existing neural retrieval models reveal promising results when training data is abundant and the performance keeps improving as training data increases, collecting high-quality annotated data is prohibitively costly. To this end, we introduce a novel noisy self-training framework combined with synthetic queries, showing that neural retrievers can be improved in a self-evolution manner with no reliance on any external models. Experimental results show that our method improves consistently over existing methods on both general-domain (e.g., MS-MARCO) and out-of-domain (i.e., BEIR) retrieval benchmarks. Extra analysis on low-resource settings reveals that our method is data efficient and outperforms competitive baselines, with as little as 30% of labelled training data. Further extending the framework for reranker training demonstrates that the proposed method is general and yields additional gains on tasks of diverse domains.</abstract>
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    <identifier type="doi">10.18653/v1/2023.findings-emnlp.803</identifier>
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%0 Conference Proceedings
%T Noisy Self-Training with Synthetic Queries for Dense Retrieval
%A Jiang, Fan
%A Drummond, Tom
%A Cohn, Trevor
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F jiang-etal-2023-noisy
%X Although existing neural retrieval models reveal promising results when training data is abundant and the performance keeps improving as training data increases, collecting high-quality annotated data is prohibitively costly. To this end, we introduce a novel noisy self-training framework combined with synthetic queries, showing that neural retrievers can be improved in a self-evolution manner with no reliance on any external models. Experimental results show that our method improves consistently over existing methods on both general-domain (e.g., MS-MARCO) and out-of-domain (i.e., BEIR) retrieval benchmarks. Extra analysis on low-resource settings reveals that our method is data efficient and outperforms competitive baselines, with as little as 30% of labelled training data. Further extending the framework for reranker training demonstrates that the proposed method is general and yields additional gains on tasks of diverse domains.
%R 10.18653/v1/2023.findings-emnlp.803
%U https://aclanthology.org/2023.findings-emnlp.803
%U https://doi.org/10.18653/v1/2023.findings-emnlp.803
%P 11991-12008

Markdown (Informal)

[Noisy Self-Training with Synthetic Queries for Dense Retrieval](https://aclanthology.org/2023.findings-emnlp.803) (Jiang et al., Findings 2023)

• Noisy Self-Training with Synthetic Queries for Dense Retrieval (Jiang et al., Findings 2023)

ACL

• Fan Jiang, Tom Drummond, and Trevor Cohn. 2023. Noisy Self-Training with Synthetic Queries for Dense Retrieval. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 11991–12008, Singapore. Association for Computational Linguistics.