Bangla Grammatical Error Detection Leveraging Transformer-based Token Classification

2024/11/13 by Shayekh Bin Islam, Islam, Shayekh Bin, Ridwanul Hasan Tanvir +3
Computer Science · #Computation and Language (cs.CL) #Educational Technology and Assessment #FOS: Computer and information sciences #Hand Gesture Recognition Systems #Handwritten Text Recognition Techniques #Machine Learning (cs.LG)

paper · pdf · doi:10.48550/arxiv.2411.08344

openalex publication_date 2024/11/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Bangla is the seventh most spoken language by a total number of speakers in the world, and yet the development of an automated grammar checker in this language is an understudied problem. Bangla grammatical error detection is a task of detecting sub-strings of a Bangla text that contain grammatical, punctuation, or spelling errors, which is crucial for developing an automated Bangla typing assistant. Our approach involves breaking down the task as a token classification problem and utilizing state-of-the-art transformer-based models. Finally, we combine the output of these models and apply rule-based post-processing to generate a more reliable and comprehensive result. Our system is evaluated on a dataset consisting of over 25,000 texts from various sources. Our best model achieves a Levenshtein distance score of 1.04. Finally, we provide a detailed analysis of different components of our system.

Related

• Bangla Grammatical Error Detection Using T5 Transformer Model
• Leveraging LLMs for Bangla Grammar Error Correction:Error Categorization, Synthetic Data, and Model Evaluation
• Leveraging Prompt-Tuning for Bengali Grammatical Error Explanation Using Large Language Models
• How Ready Are Generative Pre-trained Large Language Models for Explaining Bengali Grammatical Errors?
• "When Data is Scarce, Prompt Smarter"... Approaches to Grammatical Error Correction in Low-Resource Settings