Abstract
High-quality bilingual resources remain a critical bottleneck for advancing multilingual NLP in low-resource settings, particularly for Bangla. To mitigate this gap, we introduce BiST, a rigorously curated Bangla-English corpus for sentence-level grammatical classification, annotated across two fundamental dimensions: syntactic structure (Simple, Complex, Compound, Complex-Compound) and tense (Present, Past, Future). The corpus is compiled from open-licensed encyclopedic sources and naturally composed conversational text, followed by systematic preprocessing and automated language identification, resulting in 30,534 sentences, including 17,465 English and 13,069 Bangla instances. Annotation quality is ensured through a multi-stage framework with three independent annotators and dimension-wise Fleiss Kappa ($\kappa$) agreement, yielding reliable and reproducible labels with $\kappa$ values of 0.82 and 0.88 for structural and temporal annotation, respectively. Statistical analyses demonstrate realistic structural and temporal distributions, while baseline evaluations show that dual-encoder architectures leveraging complementary language-specific representations consistently outperform strong multilingual encoders. Beyond benchmarking, BiST provides explicit linguistic supervision that supports grammatical modeling tasks, including controlled text generation, automated feedback generation, and cross-lingual representation learning. The corpus establishes a unified resource for bilingual grammatical modeling and facilitates linguistically grounded multilingual research.
Abstract (translated)
高质量的双语资源仍是推动低资源场景下（尤其是孟加拉语）多语言NLP发展的关键瓶颈。为缓解这一缺口，我们推出了BiST——一个经过严格筛选的孟加拉语-英语句子级语法分类语料库，在句法结构（简单句、复合句、并列句、并列复合句）与时态（现在时、过去时、将来时）两个维度进行标注。该语料库基于开放授权的百科资料与自然对话文本构建，经系统预处理与自动化语言识别后，共包含30,534句（其中英语17,465句，孟加拉语13,069句）。标注质量通过三阶段框架保障：由三名独立标注者执行，并采用分维度Fleiss Kappa（κ）一致性检验，最终结构标注κ值达0.82，时态标注κ值达0.88，确保标签可靠且可复现。统计分析显示语料库呈现真实的句法与时态分布，基线实验表明利用互补语言特定表示的双编码器架构持续优于强大多语言编码器。除基准测试外，BiST提供的显性语言监督还能支持语法建模任务，包括可控文本生成、自动反馈生成与跨语言表征学习。该语料库为双语语法建模建立了统一资源，并将推动基于语言学的多语言研究发展。
URL
https://arxiv.org/abs/2604.04708
