We present Team BD’s submission to the BEA 2025 Shared Task on Pedagogical Ability Assessment of AI-powered Tutors, under Track 1 (Mistake Identification) and Track 2 (Mistake Location). Both tracks involve three-class classification of tutor responses in educational dialogues – determining if a tutor correctly recognizes a student’s mistake (Track 1) and whether the tutor pinpoints the mistake’s location (Track 2). Our system is built on MPNet, a Transformer-based language modelthat combines BERT and XLNet’s pre-training advantages. We fine-tuned MPNet on the task data using a class-weighted cross-entropy loss to handle class imbalance, and leveraged grouped cross-validation (10 folds) to maximize the use of limited data while avoiding dialogue overlap between training and validation. We then performed a hard-voting ensemble of the best models from each fold, which improves robustness and generalization by combining multiple classifiers. Ourapproach achieved strong results on both tracks, with exact-match macro-F1 scores of approximately 0.7110 for Mistake Identification and 0.5543 for Mistake Location on the official test set. We include comprehensive analysis of our system’s performance, including confusion matrices and t-SNE visualizations to interpret classifier behavior, as well as a taxonomy of common errors with examples. We hope our ensemble-based approach and findings provide useful insights for designing reliable tutor response evaluation systems in educational dialogue settings.

Stemmers are commonly used in NLP to reduce words to their root form. However, this process may discard important information and yield incorrect root forms, affecting the accuracy of NLP tasks. To address these limitations, we propose a Contextual Bangla Neural Stemmer for Bangla language to enhance word representations. Our method involves splitting words into characters within the Neural Stemming Block, obtaining vector representations for both stem words and unknown vocabulary words. A loss function aligns these representations with Word2Vec representations, followed by contextual word representations from a Universal Transformer encoder. Mean Pooling generates sentence-level representations that are aligned with BanglaBERT’s representations using a MLP layer. The proposed model also tries to build good representations for out-of-vocabulary (OOV) words. Experiments with our model on five Bangla datasets shows around 5% average improvement over the vanilla approach. Notably, our method avoids BERT retraining, focusing on root word detection and addressing OOV and sub-word issues. By incorporating our approach into a large corpus-based Language Model, we expect further improvements in aspects like explainability.

In this study, we examine and analyze the behavior of several graph-based models for Bangla text classification tasks. Graph-based algorithms create heterogeneous graphs from text data. Each node represents either a word or a document, and each edge indicates relationship between any two words or word and document. We applied the BERT model and different graph-based models including TextGCN, GAT, BertGAT, and BertGCN on five different datasets including SentNoB, Sarcasm detection, BanFakeNews, Hate speech detection, and Emotion detection datasets for Bangla text. BERT’s model bested the TextGCN and the GAT models by a large difference in terms of accuracy, Macro F1 score, and weighted F1 score. BertGCN and BertGAT are shown to outperform standalone graph models and BERT model. BertGAT excelled in the Emotion detection dataset and achieved a 1%-2% performance boost in Sarcasm detection, Hate speech detection, and BanFakeNews datasets from BERT’s performance. Whereas, BertGCN outperformed BertGAT by 1% for SetNoB, and BanFakeNews datasets while beating BertGAT by 2% for Sarcasm detection, Hate Speech, and Emotion detection datasets. We also examined different variations in graph structure and analyzed their effects.

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.