English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This paper presents a method for detecting grammatical errors in Bangla usinga Text-to-Text Transfer Transformer (T5) Language Model, using the smallvariant of BanglaT5, fine-tuned on a corpus of 9385 sentences where errors werebracketed by the dedicated demarcation symbol. The T5 model was primarilydesigned for translation and is not specifically designed for this task, soextensive post-processing was necessary to adapt it to the task of errordetection. Our experiments show that the T5 model can achieve low LevenshteinDistance in detecting grammatical errors in Bangla, but post-processing isessential to achieve optimal performance. The final average LevenshteinDistance after post-processing the output of the fine-tuned model was 1.0394 ona test set of 5000 sentences. This paper also presents a detailed analysis ofthe errors detected by the model and discusses the challenges of adapting atranslation model for grammar. Our approach can be extended to other languages,demonstrating the potential of T5 models for detecting grammatical errors in awide range of languages.

Bangla Grammatical Error Detection Using T5 Transformer Model