India

Detecting hateful memes is a complex task due to the interplay of text and visuals, with subtle cultural cues often determining whether content is harmful. This challenge is amplified in Bangla, a low-resource language where existing resources provide only binary labels or single dimensions of hate. To bridge this gap, we introduce BanHateME, a comprehensive Bangla hateful meme dataset with hierarchical annotations across three levels: binary hate, hate categories, and targeted groups. The dataset comprises 3,819 culturally grounded memes, annotated with substantial inter-annotator agreement. We further propose a hierarchical loss function that balances predictions across levels, preventing bias toward binary detection at the expense of fine-grained classification. To assess performance, we pair pretrained language and vision models and systematically evaluate three multimodal fusion strategies: summation, concatenation, and co-attention, demonstrating the effectiveness of hierarchical learning and cross-modal alignment. Our work establishes BanHateME as a foundational resource for fine-grained multimodal hate detection in Bangla and contributes key insights for content moderation in low-resource settings.

IJCNLP-AACL 2025

BanHateME : Understanding Hate in Bangla Memes thorough Detection, Categorization, and Target Profiling

bangla meme

hierarchical loss

multimodal model

dataset

### 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

Topic modeling methods identify fundamental themes within textual documents, facilitating an understanding of the insights inside them. Traditional topic modeling approaches are based on the generative probabilistic process that assumes the document-topic and topic-word distribution. Hence, those approaches fail to capture semantic similarities among words inside the documents and are less scalable with the vast number of topics and documents. This paper presents a method for capturing topics from Bangla documents by clustering the word vectors induced from LLM models. Corpus statistics are integrated into the clustering & word reordering process within each cluster or topic to extract the top words. Additionally, we deploy dimensionality reduction techniques, such as PCA, prior to clustering. Finally, we perform a comparative study and identify the best-performing combination of clustering and word embedding methods. Our top-performing combination outperforms the traditional probabilistic topic model in capturing topics and top words per topic, and excels notably in terms of computational efficiency and time complexity.

Clustering LLM-based Word Embeddings to Determine Topics from Bangla Articles

Bangla text on the internet often appears in mixed scripts that combine native Bangla characters with their Romanized transliterations. To ensure practical usability, language models should be robust to naturally occurring script mixing. Our work investigates the robustness of current LLMs and Bangla language models under various transliteration-based textual perturbations, \textit{i.e.}, we augment portions of existing Bangla datasets using transliteration. Specifically, we replace words and sentences with their transliterated text to emulate realistic script mixing, and similarly, replace the top k salient words to emulate adversarial script mixing. Our experiments reveal interesting behavioral insights and vulnerabilities to robustness in language models for Bangla, which can be crucial for deploying such models in real-world scenarios and enhancing their overall robustness.

Robustness of LLMs to Transliteration Perturbations in Bangla

Adapting large pre-trained language models (LLMs) to downstream tasks typically requires fine-tuning, but fully updating all parameters is computationally prohibitive. Parameter-Efficient Fine-Tuning (PEFT) methods like Low-Rank Adaptation (LoRA) reduce this cost by updating a small subset of parameters. However, the standard approach of jointly training LoRA adapters and a new classifier head from a cold start can lead to training instability, as the classifier chases shifting feature representations. To address this, we propose LP-FT-LoRA, a novel three-stage training framework that decouples head alignment from representation learning to enhance stability and performance. Our framework first aligns the classifier head with the frozen backbone via linear probing, then trains only the LoRA adapters to learn task-specific features, and finally performs a brief joint refinement of the head and adapters. We conduct extensive experiments on five Bangla NLP benchmarks across four open-weight compact transformer models. The results demonstrate that LP-FT-LoRA consistently outperforms standard LoRA fine-tuning and other baselines, achieving state-of-the-art average performance and showing improved generalization on out-of-distribution datasets.

TeamHateMate at BLP Task1: Divide and Conquer: A Two-Stage Cascaded Framework with K-Fold Ensembling for Multi-Label Bangla Hate Speech Classification

Millions in Bangladesh speak Sylheti and Chittagonian (Chatgaiyya) dialects, yet most public health guidance exists only in Standard Bangla, which creates barriers and safety risks. Ad-hoc translation further harms comprehension, while challenges such as scarce data, non-standard spelling, medical terms, numerals, and idioms make accurate translation difficult. We present BanglaCHQ-Prantik, the first benchmark for this setting, extending BanglaCHQ-Summ with human gold references from 17 native translators. We evaluate Qwen 2.5 3B, Gemma 3 1B, GPT-4o mini, and Gemini 2.5 Flash under zero-shot, one-shot, five-shot, and chain-of-thought prompts, using BLEU, ROUGE-1/2/L, and METEOR. Closed-source models (GPT-4o, Gemini 2.5) lead overall, with Gemini 2.5 Flash being strongest. Few-shot prompting helps especially for Sylheti, though errors persist with terminology, numerals, and idioms. The dataset is designed to support both NLP research and public health communication by enabling reliable translation across regional Bangla dialects. To our knowledge, this is the first medical-domain dataset for Sylheti/Chittagonian.

LP-FT-LoRA: A Three-Stage PEFT Framework for Efficient Domain Adaptation in Bangla NLP Tasks

Large Language Models (LLMs) have demonstrated strong translation abilities through prompting, even without task-specific training. However, their effectiveness in dialectal and low-resource contexts remains underexplored. This study presents the first systematic investigation of LLM-based Machine Translation (MT) for Sylheti, a dialect of Bangla that is itself low-resource. We evaluate five advanced LLMs (GPT-4.1, GPT-4.1-mini, LLaMA 4, Grok 3, and Deepseek V3.2) across both translation directions (Bangla ↔ Sylheti), and find that these models struggle with dialect-specific vocabulary. To address this, we introduce Sylheti-CAP (Context-Aware Prompting), a three-step framework that embeds a linguistic rulebook, dictionary (core vocabulary and idioms), and authenticity check directly into prompts. Extensive experiments show that Sylheti-CAP consistently improves translation quality across models and prompting strategies. Both automatic metrics and human evaluations confirm its effectiveness, while qualitative analysis reveals notable reductions in hallucinations, ambiguities, and awkward phrasing—establishing Sylheti-CAP as a scalable solution for dialectal and low-resource MT.

Human–LLM Benchmarks for Bangla Dialect Translation: Sylheti and Chittagonian on the BanglaCHQ-Summ Corpus

Political stance detection in understudied socio-political contexts presents a persistent challenge for language models because dynamic contexts and indirect relationships between political entities complicate the accurate alignment of opinions. To address this, we introduce GRASP-ChoQ, an approach that combines structured knowledge graphs with chain-of-questions reasoning to break down interactions in political texts. We support this with BPDisC, a novel dataset of politically charged tweets from Bangladesh during and after the July 2024 protests, along with a knowledge graph that details political entities and events. By using the knowledge graph to provide context, GRASP-ChoQ moves away from making direct predictions and instead uses intermediate reasoning steps. Experiments indicate that our proposed method yields substantial improvements relative to baseline approaches. Notably, the DeepSeek R1 variant, when integrated with GRASP-ChoQ, achieved the highest performance, demonstrating a 40% higher F1 score over zero-shot detection. As a whole, through the proposed framework, the improvement of retrieval augmentation is occurring, which facilitates the adaptive analysis of low-resource discussion of politics.

LLMs for Low-Resource Dialect Translation Using Context-Aware Prompting: A Case Study on Sylheti

The advancement of NLP technologies for low-resource and endangered languages is critically hindered by the scarcity of high-quality, parallel corpora. This is particularly true for languages like Chakma, which also faces the challenge of prevalent non-standard, romanized script usage in digital communication. To address this, we introduce ChakmaBridge, the first five-way parallel corpus for Chakma, containing 807 sentences aligned across English, Standard Bangla, Bengali-script Chakma, Romanized Bangla, and Romanized Chakma. Our dataset is created by augmenting the MELD corpus with LLM-generated romanizations that are rigorously validated by native speakers. We establish robust machine translation baselines across six diverse language and script pairs. Our experiments reveal that a multilingual training approach, combining English and Bangla as source languages, yields a dramatic performance increase, achieving a BLEU score of 0.5228 for Chakma translation, a 124\% relative improvement over the best bilingual model. We release ChakmaBridge to facilitate research in low-resource MT and aid in the digital preservation of this endangered language.

GRASP-ChoQ: Knowledge Graph-Based Retrieval Augmentation for Stance Detection in Political Texts with Chain-of-Questions Reasoning

Bangla, a language spoken by over 300 million native speakers and ranked as the sixth most spoken language worldwide, presents unique challenges in natural language processing (NLP) due to its complex morphological characteristics and limited resources. Although recent large-decoder-based LLMs, such as GPT, LLaMA, and DeepSeek, have demonstrated excellent performance across many NLP tasks, their effectiveness in Bangla remains largely unexplored. In this paper, we establish the first benchmark comparing large decoder-based LLMs with classic encoder-based models for the Zero-Shot Multi-Label Classification (Zero-Shot-MLC) task in Bangla. Our evaluation of 32 state-of-the-art models reveals that existing so-called powerful encoders and decoders still struggle to achieve high accuracy on the Bangla Zero-Shot-MLC task, suggesting a need for more research and resources for Bangla NLP.

Byte Pair Encoding Is All You Need For Automatic Bengali Speech Recognition

Poetry generation in low-resource languages such as Bangla is particularly challenging due to the scarcity of structured poetic corpora and the complexity of its metrical system (matra). We present a structure-aware framework for Bangla poetry generation using pretrained Bangla large language models (LLMs)–TigerLLM, TituLLM, and BanglaT5–trained on general non-poetic text corpora augmented with rich structural control tokens. These tokens capture rhyme, meter, word count, and line boundaries, enabling unsupervised modeling of poetic form without curated poetry datasets. Unlike prior fixed-pattern approaches, our framework introduces variable control compositions, allowing models to generate flexible poetic structures. Experiments show that explicit structural conditioning improves rhyme consistency and metrical balance while maintaining semantic coherence. Our study provides the first systematic evaluation of Bangla LLMs for form-constrained creative generation, offering insights into structural representation in low-resource poetic modeling.

Zero-Shot Multi-Label Classification of Bangla Documents: Large Decoders Vs. Classic Encoders

Depression detection from social media text is critical for early mental health intervention, yet existing NLP systems underperform in low-resource, code-mixed settings. Bangla-English code-mixing, common across South Asian online communities, poses unique challenges due to irregular grammar, transliteration, and scarce labeled data. To address this gap, we introduce DepressiveText, a 7,019-sample dataset of Bangla-English social media posts annotated for depressive signals, with strong inter-annotator agreement ($\kappa = 0.84$). We further propose a hybrid architecture that combines BanglishBERT embeddings with an LSTM classifier, enabling the model to capture both contextual and sequential cues. Comparative experiments with traditional ML, deep learning, and multilingual transformer baselines demonstrate that our approach achieves the highest performance, with an accuracy of 0.8889. We also employ LIME to enhance interpretability by identifying key lexical triggers. Our findings underscore the effectiveness of hybrid transformer–sequence models for low-resource code-mixed NLP and highlight their potential in real-world mental health applications.

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