构建5.2万对孟加拉语视觉问答数据集，提升低资源语言AI研究质量
Bangla-Bayanno: A 52K-Pair Bengali Visual Question Answering Dataset with LLM-Assisted Translation Refinement
- 用多语言大模型辅助翻译优化，减少人工误差
- 含52,650组问答对，覆盖3类答案类型和4750+图像
- 为低资源语言多模态研究提供高质量开源基准
本文提出 Bangla-Bayanno，一个面向孟加拉语的开放性视觉问答（VQA）数据集，该语言在多模态人工智能研究中属广泛使用但资源稀缺的语言。现有数据集多为特定领域、问题类型或答案格式的人工标注，或受限于小众答案形式。为降低人为错误并确保表述清晰，我们采用多语言大模型辅助的翻译精炼流程，克服了多语言源翻译质量不佳的问题。该数据集包含超过4750张图像上的52,650个问答对，问题按答案类型分为三类：名词型（简短描述）、数量型（数值）和是非型（是/否）。Bangla-Bayanno 是目前最全面、高质量的开源孟加拉语VQA基准，旨在推动低资源多模态学习研究，并促进更包容的AI系统发展。
原文摘要 · Abstract (English)
In this paper, we introduce Bangla-Bayanno, an open-ended Visual Question Answering (VQA) Dataset in Bangla, a widely used, low-resource language in multimodal AI research. The majority of existing datasets are either manually annotated with an emphasis on a specific domain, query type, or answer type or are constrained by niche answer formats. In order to mitigate human-induced errors and guarantee lucidity, we implemented a multilingual LLM-assisted translation refinement pipeline. This dataset overcomes the issues of low-quality translations from multilingual sources. The dataset comprises 52,650 question-answer pairs across 4750+ images. Questions are classified into three distinct answer types: nominal (short descriptive), quantitative (numeric), and polar (yes/no). Bangla-Bayanno provides the most comprehensive open-source, high-quality VQA benchmark in Bangla, aiming to advance research in low-resource multimodal learning and facilitate the development of more inclusive AI systems.
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