Contextual Multilingual Spellchecker for User Queries
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arXiv
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| Format: | Preprint |
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2023
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| _version_ | 1866909168312516608 |
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| author | Sharma, Sanat Valls-Vargas, Josep King, Tracy Holloway Guerin, Francois Arora, Chirag |
| author_facet | Sharma, Sanat Valls-Vargas, Josep King, Tracy Holloway Guerin, Francois Arora, Chirag |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2305_01082 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Contextual Multilingual Spellchecker for User Queries Sharma, Sanat Valls-Vargas, Josep King, Tracy Holloway Guerin, Francois Arora, Chirag Computation and Language Information Retrieval Machine Learning 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. |
| title | Contextual Multilingual Spellchecker for User Queries |
| topic | Computation and Language Information Retrieval Machine Learning |
| url | https://arxiv.org/abs/2305.01082 |