India

We present a novel Bangla Dialect Dataset comprising 600 annotated instances across four major dialects: Chattogram, Barishal, Sylhet, and Noakhali. The dataset was constructed from YouTube comments spanning diverse domains to capture authentic dialectal variations in informal online communication. Each instance includes the original dialectical text, its standard Bangla translation, and sentiment labels (Positive and Negative). We benchmark several state-of-the-art large language models on dialect-to-standard translation and sentiment analysis tasks using zero-shot and few-shot prompting strategies. Our experiments reveal that transliteration significantly improves translation quality for closed-source models, with GPT-4o-mini achieving the highest BLEU score of 0.343 in zero-shot with transliteration. For sentiment analysis, GPT-4o-mini demonstrates perfect precision, recall, and F1 scores (1.000) in few-shot settings. This dataset addresses the critical gap in resources for low-resource Bangla dialects and provides a foundation for developing dialect-aware NLP systems.

IJCNLP-AACL 2025

A Comprehensive Text Optimization Approach to Bangla Summarization

bansdata

instruction-tuned llms

two-phase optimization

sentence ranking

named entity recognition (ner)

mbart

bangla

human evaluation

low-resource nlp

bert embeddings

grammatical error correction

factual consistency

abstractive summarization

### 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

Automatic image captioning has gained significant attention at the intersection of computer vision and natural language processing, yet research in low-resource languages such as Bangla remains limited. This work introduces BiCap, an attention-based encoder–decoder framework designed for Bangla image captioning. The model leverages a pretrained ResNet-50 as the encoder to extract rich visual features and a Long Short-Term Memory (LSTM) network as the decoder to sequentially generate Bangla captions. To overcome the fixed-length bottleneck of traditional encoder–decoder architectures, we integrate Bahdanau attention, enabling the decoder to dynamically focus on salient image regions while producing each word. The model is trained and evaluated on the Chitron dataset, with extensive preprocessing including vocabulary construction, tokenization, and word embedding. Experimental results demonstrate that BiCap achieves superior performance over the existing works (Masud et al., 2025; Hossain et al., 2024; Das et al., 2023; Humaira et al., 2021), yielding higher BLEU, METEOR, ROUGE, CIDEr scores. Improved fluency in human evaluation further confirms that the model generates more contextually accurate and semantically coherent captions, although occasional challenges remain with complex scenes. Recent advances in Vision–Language Models (VLMs), such as CLIP, BLIP, Flamingo, LLaVA, and MiniGPT-4, have redefined state-of-the-art captioning performance in high-resource settings. However, these models require large multimodal corpora and extensive pretraining that are currently unavailable for Bangla. BiCap therefore offers a resource-efficient, interpretable, and practically deployable solution tailored to low-resource multimodal learning.

Benchmarking Large Language Models on Bangla Dialect Translation and Dialectal Sentiment Analysis

We study sentence-level generative data augmentation for Bangla semantic classification across four public datasets and three pretrained model families (BanglaBERT, XLM-Indic, mBERT). We evaluate two widely used, reproducible techniques—paraphrasing (mT5-based) and round-trip backtranslation (Bn–En–Bn)—and analyze their impact under realistic class imbalance. Overall, augmentation often helps, but gains are tightly coupled to label quality: paraphrasing typically outperforms backtranslation and yields the most consistent improvements for the monolingual model, whereas multilingual encoders benefit less and can be more sensitive to noisy minority-class expansions. A key empirical observation is that the neutral class appears to be a major source of annotation noise, which degrades decision boundaries and can cap the benefits of augmentation even when positive/negative classes are clean and polarized. We provide practical guidance for Bangla sentiment pipelines: (i) use simple sentence-level augmentation to rebalance classes when labels are reliable; (ii) allocate additional curation and higher inter-annotator agreement targets to the neutral class. Our results indicate when augmentation helps and suggest that data quality—not model choice alone—can become the limiting factor.

A Comparative Analysis of Retrieval-Augmented Generation Techniques for Bengali Standard-to-Dialect Machine Translation Using LLMs

Aspect-Based Sentiment Analysis (ABSA) identifies sentiments toward specific aspects of an entity. While progress has been substantial for high-resource languages such as English, low-resource languages like Bangla remain underexplored due to limited annotated data and linguistic challenges. We propose Gen-mABSA-T5, a multilingual zero-shot generative framework for ABSA based on Flan-T5, incorporating prompt engineering and Natural Language Inference (NLI). Without task-specific training, Gen-mABSA-T5 achieves state-of-the-art zero-shot accuracy of 61.56% on the large Bangla corpus, 73.50% on SemEval Laptop, and 73.56% on SemEval Restaurant outperforming both English and Bangla task-specific models in zero-shot settings. It delivers reasonable performance against very large general-purpose models on both English and Bangla benchmarks. These results highlight the effectiveness of generative, zero-shot approaches for ABSA in low-resource and multilingual settings.

Generative Data Augmentation for Improving Semantic Classification

Online discourse in Bangla is rife with nuanced toxicity expressed through code-mixing, dialectal variation, and euphemism. Effective moderation thus requires fine-grained detection of hate type, target, and severity, rather than a binary label. To address this, we organized the Bangla Hate Speech Identification Shared Task at the BLP 2025 workshop, co-located with IJCNLP-AACL 2025, comprising three subtasks: (1A) hate-type detection, (1B) hate-target detection, and (1C) joint prediction of type, target, and severity in a multi-task setup. The subtasks attracted 161, 103, and 90 participants, with 36, 23, and 20 final submissions, respectively, while a total of 19 teams submitted system description papers. The submitted systems employed a wide range of approaches, ranging from classical machine learning to fine-tuned pretrained models and zero-/few-shot LLMs. We describe the task setup, datasets, and evaluation framework, and summarize participant systems. All datasets and evaluation scripts are publicly released.

Gen-mABSA-T5: A Multilingual Zero-Shot Generative Framework for Aspect-Based Sentiment Analysis

This paper presents an overview of the BLP 2025 shared task Code Generation in Bangla, organized with the BLP workshop co-located with AACL. The task evaluates Generative AI systems capable of generating executable Python code from natural language prompts written in Bangla. This is the first shared task to address Bangla code generation. It attracted 152 participants across 63 teams, yielding 488 submissions, with 15 system-description papers. Participating teams employed both proprietary and open-source LLMs, with prevalent strategies including prompt engineering, fine-tuning, and machine translation. The top Pass@1 reached 0.99 on the development phase and 0.95 on the test phase. In this report, we detail the task design, data, and evaluation protocol, and synthesize methodological trends observed across submissions. Notably, we observe that the high performance is not based on single models; rather, a pipeline of multiple AI tools and/or methods.

Code_Gen at BLP-2025 Task 1: Enhancing Bangla Hate Speech Detection with Transformers through Token-Aware Adversarial Contrastive Training and Layer-wise Learning Rate Decay

Detecting hate speech on social media is essential for safeguarding online communities, yet it remains challenging for low-resource languages like Bangla due to class imbalance and subjective annotations. We introduce a two-stage cascaded framework with $k$-fold ensembling to address the BLP Workshop 2025 Shared Task’s three subtasks: 1A (hate type classification), 1B (target identification), and 1C (joint classification of type, target, and severity). Our solution balances precision and recall, achieving micro-$F1$ scores of $0.7331$ on 1A, $0.7356$ on 1B, and $0.7392$ on 1C, ranking 4th on 1A and 1st on both 1B and 1C. It performs strongly on major classes, although underrepresented labels such as sexism and mild severity remain challenging. Our method makes the optimal use of limited data through $k$-fold ensembling and delivers overall balanced performance across majority and minority classes by mitigating class imbalance via cascaded layers.

CUET-NLP_Zenith at BLP-2025 Task 1: A Multi-Task Ensemble Approach for Detecting Hate Speech in Bengali YouTube Comments

Bangla social media contains several types of hate speech and slurs, but automatic detection is tough due to linguistic complexity, data imbalance and limited resources. We address this challenge in the BLP-2025 shared task by combining Token-Aware Adversarial Contrastive Training (TACT) with Layer-wise Learning Rate Decay (LLRD) to fine-tune transformer models like BanglaBERT, MuRIL, mE5-base and Twitter XLM-R. To capture the complementary strengths of each model, we aggregate the model outputs through logits ensembling and get a robust system for multiclass classification. On the official test set, our model achieved F1 scores of 0.7362 for hate type, 0.7335 for severity, and 0.7361 for target ranking, placing it 1st, 2nd, and 3rd, respectively. The findings indicate that adversarial fine-tuning with logits ensemble learning is a robust way to detect hate speech in resource-limited languages and provides valuable insights for multilingual and low-resource NLP research.

Overview of BLP-2025 Task 1: Bangla Hate Speech Identification

Hate speech on social media platforms, particularly in low-resource languages like Bengali, poses a significant challenge due to its nuanced nature and the need to understand its type, severity, and targeted group. To address this, the Bangla Multi-task Hate Speech Identification Shared Task at BLP 2025 adopts a multi-task learning framework that requires systems to classify Bangla YouTube comments across three subtasks simultaneously: type of hate, severity, and targeted group. To tackle these challenges, this work presents BanTriX, a transformer ensemble method that leverages BanglaBERT-I, XLM-R, and BanglaBERT-II. Evaluation results show that the BanTriX, optimized with cross-entropy loss, achieves the highest weighted micro F1-score of 73.78\% in Subtask 1C, securing our team 2nd place in the shared task.

PromptGuard at BLP-2025 Task 1: A Few-Shot Classification Framework Using Majority Voting and Keyword Similarity for Bengali Hate Speech Detection

This paper describes HateSense, our multi-task learning framework for the BLP 2025 shared task 1 on Bangla hate speech identification. The task requires not only detecting hate speech but also classifying its type, target, and severity. HateSense integrates binary and multi-label classifiers using both encoder- and decoder-based large language models (LLMs). We experimented with pre-trained encoder models (Bert based models), and decoder models like GPT-4.0, LLaMA 3.1 8B, and Gemma-2 9B. To address challenges such as class imbalance and the linguistic complexity of Bangla, we employed techniques like focal loss and odds ratio preference optimization (ORPO). Experimental results demonstrated that the pre-trained encoders (BanglaBert) achieved state-of-the-art performance. Among different prompting strategies, chain-of-thought (CoT) combined with few-shot prompting proved most effective. Following the HateSense framework, our system attained competitive micro-F1 scores: 0.741 (Task 1A), 0.724 (Task 1B), and 0.7233 (Task 1C). These findings affirm the effectiveness of transformer-based architectures for Bangla hate speech detection and suggest promising avenues for multi-task learning in low-resource languages.

Ecstasy at BLP-2025 Task 1: TF-IDF Informed Prompt Engineering with LoRA Fine-tuning for Bangla Hate Speech Detection

We present a hybrid approach for Bangla hate speech detection that combines linguistic analysis with neural fine tuning. Our method first identifies category specific keywords using TF-IDF analysis on 35,522 training samples. These keywords then inform prompt engineering for Llama 3.1 8B model fine tuned with LoRA adapters. We incorporate distinctive Bangla terms directly into classification prompts to guide the model understanding of hate speech patterns. Our system achieved top 5 rankings across all three BLP 2025 Task 1 subtasks including hate type classification, target identification, and multi task prediction. The approach proved particularly effective for culturally specific hate speech patterns unique to Bangla social media discourse.

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Benchmarking Large Language Models on Bangla Dialect Translation and Dialectal Sentiment Analysis

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