Bangla (Bengali) is the fifth most spoken language globally and, yet, the
problem of automatic grammar correction in Bangla is still in its nascent
stage. This is mostly due to the need for a large corpus of grammatically
incorrect sentences, with their corresponding correct counterparts. The present
state-of-the-art techniques to curate a corpus for grammatically wrong
sentences involve random swapping, insertion and deletion of words.
However,these steps may not always generate grammatically wrong sentences in
Bangla. In this work, we propose a pragmatic approach to generate grammatically
wrong sentences in Bangla. We first categorize the different kinds of errors in
Bangla into 5 broad classes and 12 finer classes. We then use these to generate
grammatically wrong sentences systematically from a correct sentence. This
approach can generate a large number of wrong sentences and can, thus, mitigate
the challenge of lacking a large corpus for neural networks. We provide a
dataset, Vaiyakarana, consisting of 92,830 grammatically incorrect sentences as
well as 18,426 correct sentences. We also collected 619 human-generated
sentences from essays written by Bangla native speakers. This helped us to
understand errors that are more frequent. We evaluated our corpus against
neural models and LLMs and also benchmark it against human evaluators who are
native speakers of Bangla. Our analysis shows that native speakers are far more
accurate than state-of-the-art models to detect whether the sentence is
grammatically correct. Our methodology of generating erroneous sentences can be
applied for most other Indian languages as well.

为缺乏巨大的语料库提供了一个实用的方法来生成孟加拉语的语法错误句子，该方法分类了孟加拉语中的不同错误类型，并从正确的句子中系统地生成错误的句子，该方法提供了一个包含 92,830 个语法错误句子和 18,426 个正确句子的数据集，该数据集还通过收集 619 个孟加拉语母语者撰写的文章中的句子，帮助我们了解更常见的错误。通过与神经模型、LLM 和母语为孟加拉语的人类评估者进行了对比评估，结果表明母语为孟加拉语的人类评估者比先进的模型更准确地检测句子的语法正确性。这种生成错误句子的方法也可以应用于其他印度语言。