首次将状态空间模型用于印地语系问答，提升语言理解与生成效果。
Multilingual State Space Models for Structured Question Answering in Indic Languages
- 用状态空间模型捕捉印地语系长短期语言依赖关系。
- 在多语言数据集上显著提升问题理解与答案生成准确率。
- 针对低资源场景优化模型，适合研究印度语言NLP的学者。
印地语系语言的多样性与复杂性给自然语言处理任务带来独特挑战，尤其在问答领域。本文探索状态空间模型（SSMs）在构建面向印地语系语言的高效、上下文感知问答系统中的应用。由于其建模序列数据中长短时依赖的能力，SSMs特别适合应对印度语言丰富的词法形态、复杂句法及语境特征。我们在涵盖多种印地语系语言的多个数据集上评估了多种SSM架构，并进行了性能对比分析。结果表明，这些模型能有效捕捉语言细微差别，显著提升问题解析、上下文对齐与答案生成能力。本工作是首个将SSMs应用于印地语系问答任务的研究，建立了该领域的基础基准。我们还提出了对现有SSM框架的改进，优化其在低资源与多语言场景下的适用性。
原文摘要 · Abstract (English)
The diversity and complexity of Indic languages present unique challenges for natural language processing (NLP) tasks, particularly in the domain of question answering (QA).To address these challenges, this paper explores the application of State Space Models (SSMs),to build efficient and contextually aware QA systems tailored for Indic languages. SSMs are particularly suited for this task due to their ability to model long-term and short-term dependencies in sequential data, making them well-equipped to handle the rich morphology, complex syntax, and contextual intricacies characteristic of Indian languages. We evaluated multiple SSM architectures across diverse datasets representing various Indic languages and conducted a comparative analysis of their performance. Our results demonstrate that these models effectively capture linguistic subtleties, leading to significant improvements in question interpretation, context alignment, and answer generation. This work represents the first application of SSMs to question answering tasks in Indic languages, establishing a foundational benchmark for future research in this domain. We propose enhancements to existing SSM frameworks, optimizing their applicability to low-resource settings and multilingual scenarios prevalent in Indic languages.
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