# Contextual Multilingual Spellchecker for User Queries

arXiv: https://arxiv.org/abs/2305.01082 · HTML: https://mlsanity.com/paper/2305.01082/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 16 references that are in the map.
- First posted on arXiv 1 May 2023.

## Builds on (most cited first, 3 of 3)

- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [NeuSpell: A Neural Spelling Correction Toolkit](https://mlsanity.com/paper/2010.11085/) (2020)
- [Spelling Correction with Denoising Transformer](https://mlsanity.com/paper/2105.05977/) (2021)

## Built on by (most cited first, 2 of 2)

- [Multi-teacher Distillation for Multilingual Spelling Correction](https://mlsanity.com/paper/2311.11518/) (2023)
- [Smart Multi-Modal Search: Contextual Sparse and Dense Embedding Integration in Adobe Express](https://mlsanity.com/paper/2408.14698/) (2024)

## Abstract (arXiv)

Spellchecking is one of the most fundamental and widely used search features. Correcting incorrectly spelled user queries not only enhances the user experience but is expected by the user. However, most widely available spellchecking solutions are either lower accuracy than state-of-the-art solutions or too slow to be used for search use cases where latency is a key requirement. Furthermore, most innovative recent architectures focus on English and are not trained in a multilingual fashion and are trained for spell correction in longer text, which is a different paradigm from spell correction for user queries, where context is sparse (most queries are 1-2 words long). Finally, since most enterprises have unique vocabularies such as product names, off-the-shelf spelling solutions fall short of users' needs. In this work, we build a multilingual spellchecker that is extremely fast and scalable and that adapts its vocabulary and hence speller output based on a specific product's needs. Furthermore, our speller out-performs general purpose spellers by a wide margin on in-domain datasets. Our multilingual speller is used in search in Adobe products, powering autocomplete in various applications.