发表机构
University of Moratuwa（莫拉图瓦大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出多阶段计算框架，结合嵌入对齐与上下文剪枝，分析僧伽罗语13至20世纪语义变化，发现语义漂移主要由少数高影响实例驱动。
AI 中文摘要
在广泛的历史时间线上追踪低资源语言的语义变化，由于数据稀缺和静态嵌入对齐的局限性，面临着重大挑战。本研究采用多阶段计算框架，调查了僧伽罗语从13世纪到20世纪的历时演变。我们首先使用基于相似度矩阵的对齐（SMA）和正交普鲁克斯特（OP）技术，对齐按世纪划分的Word2Vec和FastText嵌入，发现OP对齐在识别时间相似性下降方面提供了更稳定的邻域追踪。为超越聚合度量，我们引入了一种使用来自微调Llama-3.1-8B的上下文嵌入的双向语义影响剪枝方法。通过应用留一法（LOO）诊断，我们尝试隔离有影响力的句子，以区分系统性语义转变和短暂的多义扩展。我们的结果表明，微调Llama-3.1-8B中的语义漂移并非均匀分布在所有用法中。相反，变化的很大一部分是由较小的高影响上下文实例集驱动的，而非所有出现中的逐渐且均匀的变化。这项工作为低资源语境下的历时分析提供了一个初步框架，突出了模型敏感性与数据可用性之间的权衡。
英文摘要
Tracking semantic change in low-resource languages across extensive historical timelines presents significant challenges due to data scarcity and the limitations of static embedding alignments. This study investigates the diachronic evolution of the Sinhala language from the 13th to the 20th century using a multi-stage computational framework. We first align century-specific Word2Vec and FastText embeddings using Similarity Matrix Based Alignment (SMA) and Orthogonal Procrustes (OP) techniques, finding that OP alignment provides more stable neighbourhood tracking for identifying temporal similarity dips. To move beyond aggregate measures, we introduce a Bidirectional Semantic Impact Pruning approach using contextualised embeddings from a fine-tuned Llama-3.1-8B. By applying Leave-One-Out (LOO) diagnostics, we attempt to isolate influential sentences to distinguish between systemic semantic shifts and transient polysemic expansion. Our results show that semantic drift in the fine-tuned Llama-3.1-8B is not evenly distributed across all usages. Instead, a significant part of the change is driven by a smaller set of high-impact contextual instances, rather than gradual and uniform change across all occurrences. This work provides a preliminary framework for low-resource Sinhala diachronic analysis, highlighting the trade-offs between model sensitivity and data availability.
Comments31 pages, 5 figures, 18 tables, Accepted paper at the 5th Asia-Pacific Chapter of the Association for Computational Linguistics (AACL) & the 15th International Joint Conference on Natural Language Processing (IJCNLP) 2026
