  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.
