ARABIC SOFT SPELLING CORRECTION WITH T5

Spelling correction is considered a challenging task for resource-scarce languages. The Arabic language is one of these resource-scarce languages, which suffers from the absence of a large spelling correction dataset, thus datasets injected with artificial errors are used to overcome this problem. I...

Full description

Saved in:
Bibliographic Details
Published in:Jordanian journal of computers and information technology (Online) 2024-03, Vol.10 (1), p.46-57
Main Authors: Al-Qaraghuli, Mohammed, Jaafar, Ola Arif
Format: Article
Language:English
Subjects:
Arabic language
arabic spelling correction
Information technology
natural language processing
Spelling
Students
text-to-text transfer transformer
transformers
Writing
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Spelling correction is considered a challenging task for resource-scarce languages. The Arabic language is one of these resource-scarce languages, which suffers from the absence of a large spelling correction dataset, thus datasets injected with artificial errors are used to overcome this problem. In this paper, we trained the Text-to-Text Transfer Transformer (T5) model using artificial errors to correct Arabic soft spelling mistakes. Our T5 model can correct 97.8% of the artificial errors that were injected into the test set. Additionally, our T5 model achieves a character error rate (CER) of 0.77% on a set that contains real soft spelling mistakes. We achieved these results using a 4-layer T5 model trained with a 90% error injection rate, with a maximum sequence length of 300 characters.
ISSN:2413-9351
2415-1076
DOI:10.5455/jjcit.71-1699768515