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            <namePart type="given">Masaru</namePart>
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    <abstract>Meeting the expectations of e-commerce customers involves offering a seamless online shopping experience in their preferred language. To achieve this, modern e-commerce platforms rely on machine translation systems to provide multilingual product information on a large scale. However, maintaining high-quality machine translation that can keep up with the ever-expanding volume of product data remains an open challenge for industrial machine translation systems. In this context, topical clustering emerges as a valuable approach, leveraging latent signals and interpretable textual patterns to potentially enhance translation quality and facilitate industry-scale translation data discovery. This paper proposes two innovative methods: topic-based data selection and topic-signal augmentation, both utilizing latent topic clusters to improve the quality of machine translation in e-commerce. Furthermore, we present a data discovery workflow that utilizes topic clusters to effectively manage the growing multilingual product catalogs, addressing the challenges posed by their expansion.</abstract>
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%0 Conference Proceedings
%T Leveraging Latent Topic Information to Improve Product Machine Translation
%A Zhang, Bryan
%A Walter, Stephan
%A Misra, Amita
%A Tan, Liling
%Y Yamada, Masaru
%Y do Carmo, Felix
%S Proceedings of Machine Translation Summit XIX, Vol. 2: Users Track
%D 2023
%8 September
%I Asia-Pacific Association for Machine Translation
%C Macau SAR, China
%F zhang-etal-2023-leveraging-latent
%X Meeting the expectations of e-commerce customers involves offering a seamless online shopping experience in their preferred language. To achieve this, modern e-commerce platforms rely on machine translation systems to provide multilingual product information on a large scale. However, maintaining high-quality machine translation that can keep up with the ever-expanding volume of product data remains an open challenge for industrial machine translation systems. In this context, topical clustering emerges as a valuable approach, leveraging latent signals and interpretable textual patterns to potentially enhance translation quality and facilitate industry-scale translation data discovery. This paper proposes two innovative methods: topic-based data selection and topic-signal augmentation, both utilizing latent topic clusters to improve the quality of machine translation in e-commerce. Furthermore, we present a data discovery workflow that utilizes topic clusters to effectively manage the growing multilingual product catalogs, addressing the challenges posed by their expansion.
%U https://aclanthology.org/2023.mtsummit-users.10
%P 109-118

Markdown (Informal)

[Leveraging Latent Topic Information to Improve Product Machine Translation](https://aclanthology.org/2023.mtsummit-users.10) (Zhang et al., MTSummit 2023)

• Leveraging Latent Topic Information to Improve Product Machine Translation (Zhang et al., MTSummit 2023)

ACL

• Bryan Zhang, Stephan Walter, Amita Misra, and Liling Tan. 2023. Leveraging Latent Topic Information to Improve Product Machine Translation. In Proceedings of Machine Translation Summit XIX, Vol. 2: Users Track, pages 109–118, Macau SAR, China. Asia-Pacific Association for Machine Translation.