Hierarchical Transformer Encoders for Vietnamese Spelling Correction

Tran, Hieu; Dinh, Cuong V.; Phan, Long; Nguyen, Son Truong

doi:10.1007/978-3-030-79457-6_46

Cited by 6 publications

(1 citation statement)

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“…Therefore, with the recent development of deep learning, pre-trained neural network-based language models can be applied to detect and correct spelling errors, yielding good results due to their ability to represent better language performance. In a recent investigation (Tran, Dinh, Phan, & Nguyen, 2021), a Hierarchical Transformer model was introduced for the correction of spelling errors in Vietnamese. This model utilizes two Transformer encoders: one at the character level and the other at the word level, enabling the representation of each word from both character and word perspectives.…”

Section: Theoretical Basismentioning

confidence: 99%

Detecting spelling errors in Vietnamese administrative document using large language models

Phung,

Luong

2024

HCMCOUJS - ENGINEERING AND TECHNOLOGY

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In the context of the emergence of more and more administrative documents, the need to ensure accuracy and improve the quality of these documents becomes increasingly important. This research focuses on applying advanced language models to detect spelling errors in administrative documents. Specifically, in this study, a new method using a language model based on the Transformers architecture is proposed to automatically detect and correct common spelling errors in administrative documents. This method combines the model’s ability to understand context and grammar to identify words or phrases that are likely to be misspelled. The proposed method is tested on a dataset containing real administrative documents, and the experimental results show that the proposed model is capable of detecting spelling errors with significant performance, helping to improve accuracy. and improve the quality of administrative documents. This research not only contributes to improving the quality of administrative documents but also opens up new research directions in applying language models to issues related to natural language processing in the field of administration.

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Section: Theoretical Basismentioning

confidence: 99%