BanglaMamba：探索用于孟加拉语假新闻检测的状态空间模型
BanglaMamba: Exploring State Space Models for Bangla Fake News Detection
- BRAC University（BRAC大学）
机构由 AI 辅助整理，请以论文原文为准。
中文总结 AI 辅助
本文提出BanglaMamba，将基于Mamba的状态空间模型用于孟加拉语假新闻检测，其性能与从头训练的CustomBERT相当，且推理效率优于BERT模型，凸显了该模型在资源受限场景的应用潜力。
中文摘要 AI 辅助
由于错误信息通过在线新闻平台和社交媒体快速传播，假新闻检测已成为一项重要的自然语言处理（NLP）任务。尽管诸如BanglaBERT之类的基于Transformer的模型在孟加拉语文本分类中表现出强劲性能，但其二次计算复杂度使其不太适合资源受限环境下的长文档处理。本文研究基于Mamba的状态空间模型（SSMs）作为孟加拉语假新闻检测的高效替代方案，提出了BanglaMamba并将其与预训练的BanglaBERT以及从头开始训练的同等配置BERT模型进行比较。实验结果显示，BanglaBERT的Macro-F1分数最高，为0.9260；BanglaMamba的Macro-F1分数为0.9029，尽管架构不同，但其性能与从头训练的CustomBERT（0.9057）相当。同时，BanglaMamba的推理吞吐量比基于BERT的模型高约2.2倍，推理峰值GPU内存使用率低49%。跨数据集评估显示，BanglaBERT对外部数据集的泛化能力更强，凸显了大规模预训练的重要性。这些发现表明，基于Mamba的SSMs可作为基于Transformer架构的具有竞争力且计算高效的替代方案，用于孟加拉语假新闻检测，尤其适用于资源受限的场景。
英文摘要
Fake news detection has become an important Natural Language Processing (NLP) task due to the rapid spread of misinformation through online news platforms and social media. While transformer-based models such as BanglaBERT achieve strong performance for Bangla text classification, their quadratic computational complexity makes them less suitable for long-document processing in resource-constrained environments. This paper investigates Mamba-based State Space Models (SSMs) as an efficient alternative for Bangla fake news detection. We propose BanglaMamba and compare it with pre-trained BanglaBERT and a similarly configured BERT model trained from scratch. Experimental results show that BanglaBERT achieves the highest Macro-F1 score (0.9260), while BanglaMamba (0.9029) achieves performance comparable to the from-scratch CustomBERT (0.9057) despite using a different architecture. Meanwhile, BanglaMamba achieves approximately $2.2\times$ higher inference throughput and 49% lower inference peak GPU memory usage than the BERT-based models. Cross-dataset evaluation shows that BanglaBERT generalizes better to an external dataset, highlighting the importance of large-scale pretraining. These findings demonstrate that Mamba-based SSMs can provide a competitive and computationally efficient alternative to Transformer-based architectures for Bangla fake news detection, particularly in resource-constrained settings.
