India

Hate speech detection in Bangla is challenging due to complex morphology, frequent code mixing, and severe class imbalance across categories such as abuse, sexism, religious and political hate, profanity, and neutrality. The BLP Workshop 2025 Subtask 1A addressed this by classifying Bangla YouTube comments into these categories to support online moderation in low-resource settings. We developed a BanglaBERT-based system with balanced data augmentation and advanced regularization techniques, combined with optimized training strategies for better generalization. On the blind test set, our system achieved a micro F1 score of 0.7013, ranking 21st on the leaderboard. These results indicate that augmentation, robust loss functions, and model refinements can enhance Bangla hate speech detection, though implicit and context-dependent hate speech remains difficult.

IJCNLP-AACL 2025

PerceptionLab at BLP-2025 Task 1: Domain-Adapted BERT for Bangla Hate Speech Detection: Contrasting Single-Shot and Hierarchical Multiclass Classification

few shot prompting

bangla hate speech classification

hate speech detection in low resource language

domain adapted pretraining

masked language modelling

### Welcome to IJCNLP-AACL 2025! 
 It is a great honor to host this joint conference in Mumbai, India, from December 20 to 24, 2025. The joint conferences of IJCNLP and AACL are organized with alternating leadership in the Asia-Pacific region. The event is run by the Asian Federation of Natural Language Processing (AFNLP) in odd years, and by AACL in even years, while it is organized solely by ACL when the annual ACL meeting is held in the region. This year, the conference is primarily organized by AFNLP. 
*Kentaro Inui
MBZUAI, UAE
General Chair, IJCNLP-AACL 2025* 
Read full message and download the Conference Handbook [**here**](https://drive.google.com/file/d/1UTwxkAqSqI-GAoJC3wE1zZt5VP1Y8GX0/view?usp=sharing).

The 14th IJCNLP & 4th AACL will be held in Mumbai, India from December 20th to December 24th, 2025.

workshop paper

This paper presents PerceptionLab’s approach for the BLP-2025 Shared Task 1A on multiclass Bangla hate speech detection, addressing severe class imbalance and informal online discourse. We perform Domain-Adaptive Pretraining (DAPT) on BERT models using a curated corpus of over 315,000 social media comments to capture slang, non-standard spellings, and contextual nuances of online discourse. To enrich underrepresented categories, we align external resources and construct a novel Bangla sexism dataset of over 6,800 comments via weak supervision and manual verification. Two classification strategies are compared: a single-shot six-way classifier and a two-stage hierarchical model that first separates Hate from Non-hate before fine-grained categorization. Experimental results show that single-shot classification with DAPT-enhanced BUET-BERT achieves the highest micro-F1 score (0.7265), outperforming the hierarchical approach and benchmarked general-purpose Large Language Models. Error analysis reveals persistent challenges in detecting subtle sexism and context-dependent religious hate. Our findings highlight the value of domain adaptation, robust end-to-end modeling, and targeted dataset construction for improving fine-grained hate speech detection in low-resource settings.

PentaML at BLP-2025 Task 1: Linear Probing of Pre-trained Transformer-based Models for Bangla Hate Speech Detection

This paper presents our approach for the BLP Shared Task 1, where we implemented Linear Probing of Pre-trained Transformer-based Models for Bangla Hate Speech Detection. The goal of the task was to customize the existing models so that they're capable of automatically identifying hate speech in Bangla social media text, with a focus on YouTube comments. Our approach relied on fine-tuning several pre-trained BERT models, adapting them to the shared task dataset for improved classification accuracy. To further enhance performance, we applied linear probing on three of the fine-tuned models, enabling more effective utilization of the learned representations. The combination of these strategies resulted in a consistent top-15 ranking across all subtasks of the competition. Our findings highlight the effectiveness of linear probing as a lightweight yet impactful technique for enhancing hate speech detection in low-resource languages like Bangla.

HateNet-BN at BLP-2025 Task 1: A Hierarchical Attention Approach for Bangla Hate Speech Detection

The rise of social media in Bangladesh has increased abusive and hateful content, which is difficult to detect due to the informal nature of Bangla and limited resources. The BLP 2025 shared task addressed this challenge with Subtask 1A (multi-label abuse categories) and Subtask 1B (target identification). We propose a parameter-efficient model using a frozen BanglaBERT backbone with hierarchical attention to capture token level importance across hidden layers. Context vectors are aggregated for classification, combining syntactic and semantic features. On Subtask 1A, our frozen model achieved a micro-F1 of 0.7178, surpassing the baseline of 0.7100, while the unfrozen variant scored 0.7149. Our submissions ranked 15th (Subtask 1A) and 12th (Subtask 1B), showing that layer-wise attention with a frozen backbone can effectively detect abusive Bangla text.

Bahash-AI at BLP-2025 Task 1: Bangla Hate Speech Detection using Data Augmentation and Pre-trained Model

In recent times, internet users are frequently exposed to Hate Speech on social media platforms that have long-lasting negative impacts on their mental wellbeing and also radicalizes the society into an environment of fear and distrust. Many methods have been developed to detect and stop propagation of Hate Speech. However, there is a limitation of annotated data available for Hate Speech in Bengali language. In this work, we have used a pretrained BanglaBERT model on an extended train dataset synthesized via data augmentation techniques. Our team Bahash-AI has achieved 20th, 20th and 17th position of the 3 subtasks out of total 37, 24 and 21 total number of teams who participated in the subtasks 1A, 1B and 1C respectively for Bangla Multi-task Hatespeech Identification Shared Task at BLP Workshop with F1 scores of 0.7028, 0.6954, 0.6969 respectively.

CoU-CU-DSG at BLP-2025 Task 1: Leveraging Weighted Probabilistic Fusion of Language Models for Bangla Hate Speech Detection

Detecting hate speech in Bangla is challenging due to its complex vocabulary, spelling variations, and region-specific word usage. However, effective detection is essential to ensure safer social media spaces and to take appropriate action against perpetrators. In this study, we report our participation in Subtask A of Task 1: Bangla Hate Speech Detection (Hasan et al., 2025b). In addition to the provided 50K Bangla comments (Hasan et al., 2025a), we collected approximately 4K Bangla comments and employed several data augmentation techniques. We evaluated several transformer-based models (e.g., BanglaBERT, BanglaT5, BanglaHateBERT), achieving the best performance with a micro-F1 score of 71% and securing 18th place in the Evaluation Phase.

CUET_Sntx_Srfrs at BLP-2025 Task 1: Combining Hierarchical Classification and Ensemble Learning for Bengali Hate Speech Detection

Detecting hate speech in Bengali social media content presents considerable challenges, primarily due to the prevalence of informal language and the limited availability of annotated datasets. This study investigates the identification of hate speech in Bengali YouTube comments, focusing on classifying the type, severity, and target group. Multiple machine learning baselines and voting ensemble techniques are evaluated to address these tasks. The methodology involves text preprocessing, feature extraction using TF-IDF and Count vectors, and aggregating predictions from several models. Hierarchical classification with TF-IDF features and majority voting improves the detection of less frequent hate speech categories while maintaining robust overall performance, resulting in an $18^{th}$ place ranking and a micro F1 score of 68.42\%. Furthermore, ablation studies assess the impact of preprocessing steps and n-gram selection, providing reproducible baselines for Bengali hate speech detection. All codes and resources are publicly available at https://github.com/Hasan-Mesbaul-Ali-Taher/BLP_25_Task_1

Gradient Masters at BLP-2025 Task 1: Advancing Low-Resource NLP for Bengali using Ensemble-Based Adversarial Training for Hate Speech Detection

In this paper, we propose a system for generating Python code from Bangla prompts. Our approach fine-tunes open-source models with parameter-efficient techniques and leverages proprietary models via prompting. To enhance the reasoning of smaller models, we adopt a Chain-of-Thought (CoT) augmented fine-tuning, enabling them to learn intermediate reasoning steps before generating code. A self-refinement loop further improves performance by iteratively critiquing and correcting code based on execution feedback. We also employ few-shot prompting to guide inference more effectively. Applied to both open-source and proprietary models, this pipeline achieved its best results with Gemini 2.5 Pro, where our system ranked 4th on the competition leaderboard with a Pass@1 score of 0.85. We conclude with a detailed analysis of these findings.

NALA_MAINZ at BLP-2025 Task 2: A Multi-agent Approach for Bengali Instruction to Python Code Generation

This paper presents JGU Mainz’s winning system for the BLP-2025 Shared Task on Code Generation from Bangla Instructions. We propose a multi-agent-based pipeline. First, a code-generation agent produces an initial solution from the input instruction. The candidate program is then executed against the provided unit tests (pytest-style, assert-based). Only the failing cases are forwarded to a debugger agent, which reruns the tests, extracts error traces, and, conditioning on the error messages, the current program, and the relevant test cases, generates a revised solution. Using this approach, our submission achieved first place in the shared task with a Pass@1 score of 95.4. We also make our code public.

CUET_Expelliarmus at BLP2025 Task 2: Leveraging Instruction Translation and Refinement for Bangla-to-Python Code Generation with Open-Source LLMs

Large language models (LLMs) have recently shown strong performance in generating code from natural language prompts. However, current benchmarks are primarily focused on English overlooking low-resource languages like Bangla. This creates a critical research gap since there are no well established resources or systematic evaluations for code generation from Bangla instruction. To address the gap, we present a system that generates executable Python code from Bangla instructions. We design a two-stage pipeline where the Bangla instructions are first translated and refined into clear English version to reduce ambiguity and then the python code is generated from the refined instructions with iterative error-correction. For both instruction refinement and code generation we used the open-source GPT-20B OSS model. On the official test set our system achieves competitive results. We also analyze common errors like unclear instruction, logical mistakes, runtime issues and the need for external knowledge beyond the model’s training. Overall, our findings show that a simple translation–refinement pipeline can be an effective and low-cost approach for code generation in low-resource languages.

PyBhasha at BLP-2025 Task 2: Effectiveness of Semantic-Aware Translation and Ensembling in Bangla Code Generation

In this paper, we present our submission to Task 2 of the BLP-2025 shared task on code generation from Bangla instructions. Our approach focused on enhancing instruction quality through translation and improving model performance with a two-stage ensemble strategy. We evaluated two proprietary and several open-source models under three instruction settings: original Bangla instructions, Bangla instructions translated into English using Facebook NLLB, and instructions rewritten in English with GPT-4.1. Experimental results showed that GPT-4.1-rewritten instructions consistently achieved the highest accuracy across models. For final predictions, we used a two-stage ensemble, achieving a $\textit{pass@1}$ score of $\textbf{80.0\%}$ on the hidden test set and securing 12th place on the official leaderboard. Additionally, we conducted a qualitative analysis of selected translations to illustrate how variations in instruction phrasing influenced model outputs.

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PentaML at BLP-2025 Task 1: Linear Probing of Pre-trained Transformer-based Models for Bangla Hate Speech Detection

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PentaML at BLP-2025 Task 1: Linear Probing of Pre-trained Transformer-based Models for Bangla Hate Speech Detection