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        <title>Multi-teacher Distillation for Multilingual Spelling Correction</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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    <abstract>Accurate spelling correction is a critical step in modern search interfaces, especially in an era of mobile devices and speech-to-text interfaces. For services that are deployed around the world, this poses a significant challenge for multilingual NLP: spelling errors need to be caught and corrected in all languages, and even in queries that use multiple languages. In this paper, we tackle this challenge using multi-teacher distillation. On our approach, a monolingual teacher model is trained for each language/locale, and these individual models are distilled into a single multilingual student model intended to serve all languages/locales. In experiments using open-source data as well as customer data from a worldwide search service, we show that this leads to highly effective spelling correction models that can meet the tight latency requirements of deployed services.</abstract>
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    <identifier type="doi">10.18653/v1/2023.emnlp-industry.15</identifier>
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%0 Conference Proceedings
%T Multi-teacher Distillation for Multilingual Spelling Correction
%A Zhang, Jingfen
%A Guo, Xuan
%A Bodapati, Sravan
%A Potts, Christopher
%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 zhang-etal-2023-multi-teacher
%X Accurate spelling correction is a critical step in modern search interfaces, especially in an era of mobile devices and speech-to-text interfaces. For services that are deployed around the world, this poses a significant challenge for multilingual NLP: spelling errors need to be caught and corrected in all languages, and even in queries that use multiple languages. In this paper, we tackle this challenge using multi-teacher distillation. On our approach, a monolingual teacher model is trained for each language/locale, and these individual models are distilled into a single multilingual student model intended to serve all languages/locales. In experiments using open-source data as well as customer data from a worldwide search service, we show that this leads to highly effective spelling correction models that can meet the tight latency requirements of deployed services.
%R 10.18653/v1/2023.emnlp-industry.15
%U https://aclanthology.org/2023.emnlp-industry.15
%U https://doi.org/10.18653/v1/2023.emnlp-industry.15
%P 142-151

Markdown (Informal)

[Multi-teacher Distillation for Multilingual Spelling Correction](https://aclanthology.org/2023.emnlp-industry.15) (Zhang et al., EMNLP 2023)

• Multi-teacher Distillation for Multilingual Spelling Correction (Zhang et al., EMNLP 2023)

ACL

• Jingfen Zhang, Xuan Guo, Sravan Bodapati, and Christopher Potts. 2023. Multi-teacher Distillation for Multilingual Spelling Correction. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 142–151, Singapore. Association for Computational Linguistics.