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Spelling Correction with Denoising Transformer

2021-05-12 21:35:18
Alex Kuznetsov, Hector Urdiales
arXiv_AI
     
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.

Abstract (translated)

URL

https://arxiv.org/abs/2105.05977

PDF

https://arxiv.org/pdf/2105.05977.pdf

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