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• W3160347621 abstract "We present a novel method of performing spelling correction on short input strings, such as search queries or individual words. At its core lies a procedure for generating artificial typos which closely follow the error patterns manifested by humans. This procedure is used to train the production spelling correction model based on a transformer architecture. This model is currently served in the HubSpot product search. We show that our approach to typo generation is superior to the widespread practice of adding noise, which ignores human patterns. We also demonstrate how our approach may be extended to resource-scarce settings and train spelling correction models for Arabic, Greek, Russian, and Setswana languages, without using any labeled data." @default.
• W3160347621 created "2021-05-24" @default.
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• W3160347621 date "2021-05-12" @default.
• W3160347621 modified "2023-09-27" @default.
• W3160347621 title "Spelling Correction with Denoising Transformer." @default.
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