Training models for Natural Language Processing (NLP) requires substantial computational resources and time, posing significant challenges, especially for NLP development in Bangla, where access to high-end hardware is often limited. In this work, we explore automatic mixed precision (AMP) training as a means to improve computational efficiency without sacrificing model performance. By leveraging a dynamic mix of 16-bit and 32-bit floating-point computations, AMP lowers GPU memory requirements and speeds up training without degrading model performance. We evaluate AMP across four standard Bangla NLP tasks, namely sentiment analysis, named entity recognition, error classification, and question answering, using four transformer-based models: BanglaBERT, BanglishBERT, XLM-R, and mBERT. Our results demonstrate that AMP accelerates training by 44.5% and reduces memory consumption by 17.6%, while maintaining F-1 score within 99.7% of the full-precision baselines. This empirical study highlights AMP's potential to democratize access to state-of-the-art NLP capabilities in hardware-constrained settings by lowering computational barriers.
AI Adoption to Combat Financial Crime: Study on Natural Language Processing in Adverse Media Screening of Financial Services in English and Bangla multilingual interpretation
Institute of Business Administration - JU（贾达沃普尔大学商业管理学院）
Evaluating Health Misinformation in Low-Resource Languages: Integrating Small Language Models with a Culturally-Sensitive Responsible NLP Framework (Bangla as a Case Study)
Western Sydney University（西悉尼大学）；Microsoft（微软公司）；Excelsia College（埃克塞尔西亚学院）；Faulconbridge Health Centre（福尔康布里奇健康中心）
ANCHOLIK-NER: A Benchmark Dataset for Bangla Regional Named Entity Recognition
Department of Computer Science and Engineering（计算机科学与工程系）；Southeast University（东南大学）；Ahsanullah University of Science and Technology（阿沙努尔大学科学与技术学院）；Bangladesh University of Engineering and Technology（孟加拉工程与技术大学）
随着自然语言处理（Natural Language Processing，NLP）技术的发展，越来越多的系统希望在用户界面模块中采用NLP技术来处理用户输入，从而以自然的方式与用户进行交流。然而，这带来了速度问题。也就是说，如果NLP模块不能在持久的时间延迟内处理句子，用户将永远不会使用系统。因此，对处理时间要求严格的系统，如对话系统、Web搜索系统、自动客服系统，特别是实时系统，为了获得更快的系统响应，不得不放弃NLP模块。本文旨在解决速度问题。本文首先介绍了一个基于语料库机器学习和统计模型的句法分析器的构造，然后对该句法分析器及其算法的速度问题进行了分析。在此基础上，提出了两种加速方法：压缩词性集和句法模式剪枝，有效地提高了NLP模块中句法分析的时间效率。为了评估加速算法中的不同参数，引入了两个新的因素，PT和RT，并详细解释。最后通过实验对这些方法进行了验证，为自然语言处理的应用提供了理论依据。
Echoes of Unrest: A Multimodal NLP Framework for Early Warning of Fake News and Violence-Driven Mob Activity
Department of Computer Science and Engineering, Dhaka International University（达卡国际大学计算机科学与工程系）；Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology（孟加拉国工程技术大学计算机科学与工程系）
Maseno University（马塞诺大学）；University of Cape Town（开普敦大学）；African Institute for Mathematical Sciences - AIMS（非洲数学科学研究所）；United States International University-Africa（美国国际大学非洲分校）；Zindi（津迪平台）；Jomo Kenyatta University of Agriculture and Technology - JKUAT（乔莫·肯雅塔农业技术大学）；The University of Minnesota, Twin Cities（明尼苏达大学双城分校）；University of Manchester（曼彻斯特大学）；Songhai（松海机构）；University of Groningen（格罗宁根大学）；Stellenboch University（斯泰伦博斯大学）；Jumia Group（朱米亚集团）；Adventist University of Africa（非洲基督复临大学）；University of Nairobi（内罗毕大学）；Deep Learning Indaba（深度学习印达巴组织）；Karadeniz Technical University（卡拉德尼兹技术大学）；Datawise Africa（非洲数据智能机构）；Kisii University（基西大学）；Future Africa - University of Pretoria（比勒陀利亚大学未来非洲中心）
HausaNLP: Current Status, Challenges and Future Directions for Hausa Natural Language Processing
Imperial College London（伦敦帝国理工学院）；Northeastern University（东北大学）；Data Science for Social Impact, University of Pretoria（比勒陀利亚大学数据科学为社会影响）；Kaduna State University（卡杜纳州大学）；HausaNLP；Bayero University, Kano（卡诺巴耶罗大学）；Universiti Teknologi PETRONAS（PETRONAS科技大学）；University of the Witwatersrand（沃斯堡大学）；Federal University of Lafia（拉法联邦大学）
Restoring Rhythm: Punctuation Restoration Using Transformer Models for Bangla, A Low-Resource Language
Bangladesh Army International University of Science and Technology (BAIUST)（巴尔克理工大学）；BRAC University（BRAC大学）；North East University Bangladesh (NEUB)（北东大学）
MixSarc: A Bangla-English Code-Mixed Corpus for Implicit Meaning Identification
Department of Computer Science and Engineering, Islamic University of Technology, Dhaka, Bangladesh（伊斯兰科技大学计算机科学与工程系，达卡，孟加拉国）；Department of Computer Science, University of Illinois Chicago, Chicago, Illinois, United States of America（伊利诺伊大学芝加哥分校计算机科学系，芝加哥，伊利诺伊州，美国）
Allen Institute for Artificial Intelligence（艾伦人工智能研究所）；University of Washington（华盛顿大学）；Yale University（耶鲁大学）；New York University（纽约大学）；Carnegie Mellon University（卡内基梅隆大学）
Variator: Accelerating Pre-trained Models with Plug-and-Play Compression Modules
Tsinghua University（清华大学）；Quan Cheng Laboratory（泉城实验室）；Zhili College, Tsinghua University（清华大学志道书院）；Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）；Tencent Inc.（腾讯公司）
