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        <title>Retrieval-Enhanced Dual Encoder Training for Product Matching</title>
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            <title>Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track</title>
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            <namePart type="given">Mingxuan</namePart>
            <namePart type="family">Wang</namePart>
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        <name type="personal">
            <namePart type="given">Imed</namePart>
            <namePart type="family">Zitouni</namePart>
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    <abstract>Product matching is the task of matching a seller-listed item to an appropriate product. It is a critical task for an e-commerce platform, and the approach needs to be efficient to run in a large-scale setting. A dual encoder approach has been a common practice for product matching recently, due to its high performance and computation efficiency. In this paper, we propose a two-stage training for the dual encoder model. Stage 1 trained a dual encoder to identify the more informative training data. Stage 2 then train on the more informative data to get a better dual encoder model. This technique is a learned approach for building training data. We evaluate the retrieval-enhanced training on two different datasets: a publicly available Large-Scale Product Matching dataset and a real-world e-commerce dataset containing 47 million products. Experiment results show that our approach improved by 2% F1 on the public dataset and 9% F1 on the real-world e-commerce dataset.</abstract>
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    <identifier type="doi">10.18653/v1/2023.emnlp-industry.22</identifier>
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%0 Conference Proceedings
%T Retrieval-Enhanced Dual Encoder Training for Product Matching
%A Chiu, Justin
%Y Wang, Mingxuan
%Y Zitouni, Imed
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F chiu-2023-retrieval
%X Product matching is the task of matching a seller-listed item to an appropriate product. It is a critical task for an e-commerce platform, and the approach needs to be efficient to run in a large-scale setting. A dual encoder approach has been a common practice for product matching recently, due to its high performance and computation efficiency. In this paper, we propose a two-stage training for the dual encoder model. Stage 1 trained a dual encoder to identify the more informative training data. Stage 2 then train on the more informative data to get a better dual encoder model. This technique is a learned approach for building training data. We evaluate the retrieval-enhanced training on two different datasets: a publicly available Large-Scale Product Matching dataset and a real-world e-commerce dataset containing 47 million products. Experiment results show that our approach improved by 2% F1 on the public dataset and 9% F1 on the real-world e-commerce dataset.
%R 10.18653/v1/2023.emnlp-industry.22
%U https://aclanthology.org/2023.emnlp-industry.22
%U https://doi.org/10.18653/v1/2023.emnlp-industry.22
%P 216-222

Markdown (Informal)

[Retrieval-Enhanced Dual Encoder Training for Product Matching](https://aclanthology.org/2023.emnlp-industry.22) (Chiu, EMNLP 2023)

• Retrieval-Enhanced Dual Encoder Training for Product Matching (Chiu, EMNLP 2023)

ACL

• Justin Chiu. 2023. Retrieval-Enhanced Dual Encoder Training for Product Matching. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 216–222, Singapore. Association for Computational Linguistics.