B a TEC la C or: A Novel Dataset for B angla Text Error Classification and Correction (2023)

Abstract: In the context of the dynamic realm of Bangla communication, online users are often prone to bending the language or making errors due to various factors. We attempt to detect, categorize, and correct those errors by employing several machine learning and deep learning models. To contribute to the preservation and authenticity of the Bangla language, we introduce a meticulously categorized organic dataset encompassing 10,000 authentic Bangla comments from a commonly used social media platform. Through rigorous comparative analysis of distinct models, our study highlights BanglaBERT’s superiority in error-category classification and underscores the effectiveness of BanglaT5 for text correction. BanglaBERT achieves accuracy of 79.1% and 74.1% for binary and multiclass error-category classification while the BanglaBERT is fine-tuned and tested with our proposed dataset. Moreover, BanglaT5 achieves the best Rouge-L score (0.8459) when BanglaT5 is fine-tuned and tested with our corrected ground truths. Beyond algorithmic exploration, this endeavor represents a significant stride in enhancing the quality of digital discourse in the Bangla-speaking community, fostering linguistic precision and coherence in online interactions. The dataset and code is available at https://github.com/SyedT1/BaTEClaCor.

## introduction the bangla language is an indo-aryan language with deep historical roots. it is spoken by approximately 230 million people globally and is the 6th most spoken language in the world as stated by the cia world factbook 1 . bangla is renowned for its intricate and unique style, holding cultural and literary significance, and reflecting a rich heritage spanning generations. however, within the contemporary world of communication, particularly on platforms like social media, the fluidity of making typographical errors often results in deviations from the language's original form. so, the complexity of the bangla script with its 50 letters comprising 11 vowels and 39 consonants is often reflected in the digital landscape . 2 among the set of bangla letters, certain complex characters contribute to the challenge of writing that results in a divergence between written and spoken communication. phonetically similar alphabets in bangla share the same pronunciation or phonetic utterance that allows interchangeability and consequently leads to errors within words as shown in figure 1 @xcite . for instance, figure 1 shows the interchange of letters having similar phonetic qualities that generate error words impacting the language's authenticity and coherence @xcite . textual error detection and correction of the bangla language hold significant importance as corrected text preserves language integrity, promotes literacy, and conveys professionalism. online interactions further underscore the necessity of bangla text correction as it enables clear communication on a global scale, enhances brand reputation, facilitates cross-cultural communication, and reduces the chances of misinterpretation. notably, there have been datasets used for similar purposes, predominantly consisting of samples collected from bangla newspapers, blogs, or synthetically generated. @xcite . however, they may not fully represent the day-to-day informal and formal interactions of bangla language speakers on various online platforms where several types of errors can be more prevalent. to address this gap in the existing resources, we introduce a novel dataset for bangla text error correction named bateclacor: a novel dataset for bangla text error classification and correction. the dataset is licensed under cc -by-nc 4.0 (creative commons attribution) through a comprehensive approach, this research aligns itself with the larger goal of fostering a digitally literate and linguistically precise digital space for the bangla community. our contributions are: these contributions enhance the quality of linguistic interactions online and pave the way for a more precise and digitally literate environment for bangla speakers, fostering meaningful communication, and understanding in the digital realm. ## related work numerous endeavors have been undertaken to enhance bangla text correction despite its status as a low-resource language. notably, a bangla spellchecking technique was proposed and tested on a dictionary consisting of pairs of 50,000 correct and incorrect bangla words. n-gram models were generated for each candidate word. to identify non-word errors, a comprehensive bangla word dictionary of around 600,000 words was compiled from various online repositories, newspapers, social networking sites, and bangla blogs @xcite . the study primarily addresses wordlevel errors and may lack in encompassing the full spectrum of errors, including contextual and informal errors h.a.z. sameen presented a novel approach for bangla grammatical error detection using a t5 transformer model. the training set comprised 9385 sentence pairs, while the testing set included 5,000 test sentences (shahgir and sayeed, 2023). it's mentionable that the incorrect sentences in the paired samples were not explicitly categorized to identify specific error types, and instead, errors were indicated using a particular symbol without detailed error categorization. chowdhury rafeed introduced bspell, a cnnblended bert-based bangla spell checker @xcite . the synthetic dataset of the prothom-alo 2017 online newspaper was used for training, additionally, 6,300 errorful sentences from nayadiganta online newspaper were annotated for testing. it's essential to note that the training data's synthetic nature and the usage of newspaper text may not effectively capture the nuances of informal online interactions. another method for synthetic error dataset generation was presented using a few sets of popular newspapers mimicking bangla writing patterns. the study employed a bangla corpus consisting of 6.5 million sentences. from this corpus, 8,637 frequently occurring words were selected for analysis @xcite . the study's outcomes revealed the stochastic nature of error generation. although these studies collectively contribute significantly to the advancement of bangla text correction techniques encompassing various methodologies and datasets, we aimed to address their limitations by constructing a distinct dataset that encompasses the specific error types and reflects the real-world informality prevalent in online communication. as discussed earlier, existing bangla datasets used for textual error correction mostly featured samples derived from newspaper articles, blogs and bangla repositories or were synthetically generated. such sources often portray a formal, official use of the language, which may deviate significantly from its common application in online interactions. recognizing the need to capture the intricacies of language as it is typically used, we turned to social platforms such as youtube and facebook. the driving force behind crafting this new dataset arises from the vital significance of linguistic precision, coupled with the evolving digital environment that defines modern communication particularly among bangla-speaking internet users of bangladesh. ## conclusion in this study, we embarked on a comprehensive journey to address the critical challenge of bangla text correction leveraging both traditional machine learning and deep learning techniques along with transformer models. a pivotal milestone was the creation of a novel dataset from youtube comments that was meticulously curated and annotated. the dataset serves as the cornerstone for our investigation. we conducted a rigorous evaluation of machine learning models and deep learning models including transformer models for binary and multiclass error-category classification. the standout performance of banglabert showcased its ability to navigate complex linguistic semantics. additionally, the experimental results underscore the potential of banglat5 for improving the accuracy and robustness of correction systems in bangla user-generated text. banglabert achieves accuracy of 79.1% and 74.1% for binary and multiclass error classification while the banglabert is fine-tuned and tested with our proposed dataset. moreover, banglat5 achieves the best rouge-l score (0.8459) while banglat5 is fine-tuned and tested with our corrected ground truths. our findings underscored the transformative potential of deep learning models and emphasized the importance of dataset curation. the proposed dataset stands as a unique resource set apart from its predecessors, offering a representation of language use in online settings that are more aligned with the language patterns of bangla speakers in digital communication.