BAN-Cap: A Multi-Purpose English-Bangla Image Descriptions Dataset (2022.lrec-1)
Copied to clipboard
| Challenge: | BAN-Cap dataset is based on the widely used Flickr8k dataset, which is used to collect captions of images from qualified annotators. |
| Approach: | They propose to use a dataset to collect Bangla captions from qualified annotators and to evaluate the models for the task. |
| Outcome: | The proposed model outperforms state-of-the-art models for Bangla captioning and English-Bangla translation. |
Similar Papers
BanTH: A Multi-label Hate Speech Detection Dataset for Transliterated Bangla (2025.findings-naacl)
Copied to clipboard
Fabiha Haider, Fariha Tanjim Shifat, Md Farhan Ishmam, Md Sakib Ul Rahman Sourove, Deeparghya Dutta Barua, Md Fahim, Md Farhad Alam Bhuiyan
| Challenge: | Existing work on monolingual or binary hate classification in Bangla has not addressed the challenge of multi-label hate speech classification in underrepresented languages. |
| Approach: | They propose a multi-label transliterated Bangla hate speech dataset that translates or transliterates under-resourced text to higher-resource text before classifying the hate group(s). |
| Outcome: | The proposed approach outperforms other methods in the zero-shot setting while achieving state-of-the-art performance. |
BanHADEX: Towards Explainable HAte Speech Detection in Bangla Using Human Annotated EXplanation (2026.acl-long)
Copied to clipboard
Faisal Hossain Raquib, Akm Moshiur Rahman Mazumder, Md Fahim, Md Tahmid Hasan Fuad, Md Farhan Ishmam, Faria Sultana, M Ashraful Amin, Amin Ahsan Ali, Akmmahbubur Rahman
| Challenge: | Existing studies in Bangla focus on hate classification while overlooking interpretability. |
| Approach: | They propose to create a dataset with human-annotated labels for banla that contains 19,203 YouTube comments spanning April 2024–June 2025. |
| Outcome: | The proposed dataset outperforms existing datasets on open and closed-source LLMs on interpretability and better understanding of hate speech in linguistically rich yet under-resourced languages. |
BanglaSTEM: A Parallel Corpus and Term-Weighted Evaluation for Technical Bangla-English Translation (2026.acl-srw)
Copied to clipboard
| Challenge: | Large language models excel at technical problem solving in English but struggle when questions are posed in Bangla. |
| Approach: | They propose a dataset of 5,000 Bangla-English sentence pairs to align technical terms . they use OCR to extract matching passages from bilingual textbooks . |
| Outcome: | The proposed pipeline extracts matching passages from bilingual textbooks and uses them to align sentences and mark technical terms. |
Gold Standard Bangla OCR Dataset: An In-Depth Look at Data Preprocessing and Annotation Processes (2023.emnlp-industry)
Copied to clipboard
| Challenge: | Existing datasets designed specifically for the Bengali language have been limited. |
| Approach: | They propose to use a large collection of labeled Bangla text image datasets to improve the performance of Bangla OCR. |
| Outcome: | The proposed system is the most extensive gold standard corpus for Bangla characters and words, comprising over 4 million human-annotated images. |
BanglaByT5: Byte-Level Modelling for Bangla (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Large language models (LLMs) have achievedremarkable success across various natural lan-guage processing tasks. |
| Approach: | They propose a byte-level encoder-decoder model specifically tailored for Bangla. |
| Outcome: | The proposed model outperforms existing models in gen-erative and classification tasks and surpasses several multilingual and larger models. |
BanglaBook: A Large-scale Bangla Dataset for Sentiment Analysis from Book Reviews (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing literature on Bangla Sentiment Analysis (SA) has limited data and cross-domain adaptability. |
| Approach: | They present a large-scale dataset of Bangla book reviews with 158,065 samples . they employ a range of machine learning models to establish baselines including SVM, LSTM, and Bangla-BERT. |
| Outcome: | The proposed model improves performance over models that rely on manual features. |
BanglaAutoKG: Automatic Bangla Knowledge Graph Construction with Semantic Neural Graph Filtering (2024.lrec-main)
Copied to clipboard
| Challenge: | Bangla is underrepresented in KGs due to lack of comprehensive datasets, encoders, NER models, part-of-speech taggers, and lemmatizers. |
| Approach: | Bangla is underrepresented in KGs due to lack of comprehensive datasets, encoders, NER models, part-of-speech taggers, and lemmatizers. authors propose a framework that can automatically construct Bengali KG from any Bangla text. |
| Outcome: | The proposed framework can automatically construct Bengali KGs from any Bangla text. |
BanglaTLit: A Benchmark Dataset for Back-Transliteration of Romanized Bangla (2024.findings-emnlp)
Copied to clipboard
| Challenge: | low-resource languages like Bangla are limited by the lack of datasets. |
| Approach: | They propose a large-scale transliteration dataset and a pre-training corpus on romanized Bangla. |
| Outcome: | The proposed datasets show that the proposed methods can enrich romanized Bangla. |
BanSuite: A Unified Toolkit and Software Platform for Low-Resource NLP in Bangla (2026.eacl-demo)
Copied to clipboard
Md. Abu Sayed, Faisal Ahamed Khan, Jannatul Ferdous Tuli, Nabeel Mohammed, Mohammad Ruhul Amin, Mohammad Mamun Or Rashid
| Challenge: | Existing efforts to improve Bangla's NLP performance have focused on isolated tasks such as Part-of-Speech tagging and Named Entity Recognition (NER) but comprehensive, integrated systems for core NLP tasks such Shallow Parsing and Dependency Parser are largely absent. |
| Approach: | They propose to integrate a large-scale, manually annotated Bangla Treebank with high-quality pretrained models for POS tagging, NER, shallow parsing, and dependency parse. |
| Outcome: | The proposed system achieves strong in-domain baseline performance while maintaining high efficiency in resource usage. |
TituLLMs: A Family of Bangla LLMs with Comprehensive Benchmarking (2025.findings-acl)
Copied to clipboard
Shahriar Kabir Nahin, Rabindra Nath Nandi, Sagor Sarker, Quazi Sarwar Muhtaseem, Md Kowsher, Apu Chandraw Shill, Md Ibrahim, Mehadi Hasan Menon, Tareq Al Muntasir, Firoj Alam
| Challenge: | Existing benchmarking datasets for Bangla LLMs are not available for all languages. |
| Approach: | They present TituLLMs, the first large pretrained Bangla LLMs, available in 1b and 3b parameter sizes. |
| Outcome: | The proposed model outperforms existing models in Bangla, but not always in the first place. |