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        <title>Collective Relevance Labeling for Passage Retrieval</title>
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        <namePart type="given">Jihyuk</namePart>
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    <name type="personal">
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            <title>Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies</title>
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    <abstract>Deep learning for Information Retrieval (IR) requires a large amount of high-quality query-document relevance labels, but such labels are inherently sparse. Label smoothing redistributes some observed probability mass over unobserved instances, often uniformly, uninformed of the true distribution. In contrast, we propose knowledge distillation for informed labeling, without incurring high computation overheads at evaluation time. Our contribution is designing a simple but efficient teacher model which utilizes collective knowledge, to outperform state-of-the-arts distilled from a more complex teacher model. Specifically, we train up to \times8 faster than the state-of-the-art teacher, while distilling the rankings better. Our code is publicly available at https://github.com/jihyukkim-nlp/CollectiveKD.</abstract>
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    <identifier type="doi">10.18653/v1/2022.naacl-main.305</identifier>
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        <date>2022-07</date>
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%0 Conference Proceedings
%T Collective Relevance Labeling for Passage Retrieval
%A Kim, Jihyuk
%A Kim, Minsoo
%A Hwang, Seung-won
%Y Carpuat, Marine
%Y de Marneffe, Marie-Catherine
%Y Meza Ruiz, Ivan Vladimir
%S Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F kim-etal-2022-collective
%X Deep learning for Information Retrieval (IR) requires a large amount of high-quality query-document relevance labels, but such labels are inherently sparse. Label smoothing redistributes some observed probability mass over unobserved instances, often uniformly, uninformed of the true distribution. In contrast, we propose knowledge distillation for informed labeling, without incurring high computation overheads at evaluation time. Our contribution is designing a simple but efficient teacher model which utilizes collective knowledge, to outperform state-of-the-arts distilled from a more complex teacher model. Specifically, we train up to \times8 faster than the state-of-the-art teacher, while distilling the rankings better. Our code is publicly available at https://github.com/jihyukkim-nlp/CollectiveKD.
%R 10.18653/v1/2022.naacl-main.305
%U https://aclanthology.org/2022.naacl-main.305
%U https://doi.org/10.18653/v1/2022.naacl-main.305
%P 4141-4147

Markdown (Informal)

[Collective Relevance Labeling for Passage Retrieval](https://aclanthology.org/2022.naacl-main.305) (Kim et al., NAACL 2022)

• Collective Relevance Labeling for Passage Retrieval (Kim et al., NAACL 2022)

ACL

• Jihyuk Kim, Minsoo Kim, and Seung-won Hwang. 2022. Collective Relevance Labeling for Passage Retrieval. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4141–4147, Seattle, United States. Association for Computational Linguistics.