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        <title>Machine translation impact in E-commerce multilingual search</title>
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            <title>Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track</title>
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            <namePart type="given">Yunyao</namePart>
            <namePart type="family">Li</namePart>
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            <namePart type="given">Angeliki</namePart>
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    <abstract>Previous work suggests that performance of cross-lingual information retrieval correlates highly with the quality of Machine Translation. However, there may be a threshold beyond which improving query translation quality yields little or no benefit to further improve the retrieval performance. This threshold may depend upon multiple factors including the source and target languages, the existing MT system quality and the search pipeline. In order to identify the benefit of improving an MT system for a given search pipeline, we investigate the sensitivity of retrieval quality to the presence of different levels of MT quality using experimental datasets collected from actual traffic. We systematically improve the performance of our MT systems quality on language pairs as measured by MT evaluation metrics including Bleu and Chrf to determine their impact on search precision metrics and extract signals that help to guide the improvement strategies. Using this information we develop techniques to compare query translations for multiple language pairs and identify the most promising language pairs to invest and improve.</abstract>
    <identifier type="citekey">zhang-misra-2022-machine</identifier>
    <identifier type="doi">10.18653/v1/2022.emnlp-industry.8</identifier>
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%0 Conference Proceedings
%T Machine translation impact in E-commerce multilingual search
%A Zhang, Bryan
%A Misra, Amita
%Y Li, Yunyao
%Y Lazaridou, Angeliki
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F zhang-misra-2022-machine
%X Previous work suggests that performance of cross-lingual information retrieval correlates highly with the quality of Machine Translation. However, there may be a threshold beyond which improving query translation quality yields little or no benefit to further improve the retrieval performance. This threshold may depend upon multiple factors including the source and target languages, the existing MT system quality and the search pipeline. In order to identify the benefit of improving an MT system for a given search pipeline, we investigate the sensitivity of retrieval quality to the presence of different levels of MT quality using experimental datasets collected from actual traffic. We systematically improve the performance of our MT systems quality on language pairs as measured by MT evaluation metrics including Bleu and Chrf to determine their impact on search precision metrics and extract signals that help to guide the improvement strategies. Using this information we develop techniques to compare query translations for multiple language pairs and identify the most promising language pairs to invest and improve.
%R 10.18653/v1/2022.emnlp-industry.8
%U https://aclanthology.org/2022.emnlp-industry.8
%U https://doi.org/10.18653/v1/2022.emnlp-industry.8
%P 99-109

Markdown (Informal)

[Machine translation impact in E-commerce multilingual search](https://aclanthology.org/2022.emnlp-industry.8) (Zhang & Misra, EMNLP 2022)

• Machine translation impact in E-commerce multilingual search (Zhang & Misra, EMNLP 2022)

ACL

• Bryan Zhang and Amita Misra. 2022. Machine translation impact in E-commerce multilingual search. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 99–109, Abu Dhabi, UAE. Association for Computational Linguistics.