Bangla Grammatical Error Detection Leveraging Transformer-based Token Classification

• URL: http://arxiv.org/abs/2411.08344v1
• Date: Wed, 13 Nov 2024 05:22:45 GMT
• Title: Bangla Grammatical Error Detection Leveraging Transformer-based Token Classification
• Authors: Shayekh Bin Islam, Ridwanul Hasan Tanvir, Sihat Afnan,
• Abstract summary: We study the development of an automated grammar checker in Bangla, the seventh most spoken language in the world. Our approach involves breaking down the task as a token classification problem and utilizing state-of-the-art transformer-based models. Our system is evaluated on a dataset consisting of over 25,000 texts from various sources.
• Score: 0.0
• License:
• 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.

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