利用跨语言数据增强提升低资源巴基斯坦语言的命名实体识别（NER）性能
Enhancing NER Performance in Low-Resource Pakistani Languages using Cross-Lingual Data Augmentation
AI总结：
针对低资源巴基斯坦语言NER因缺乏标注数据和PLM支持不足的问题，提出跨语言数据增强技术生成文化合理句子，微调多语言掩码LLMs后在四种语言中显著提升性能，尤其沙姆基语和普什图语。
摘要：
命名实体识别（NER）是自然语言处理（NLP）的基础任务，在高资源语言中已取得显著进展。但由于缺乏标注数据集及预训练语言模型（PLMs）中的表示有限，低资源语言的NER研究不足且具挑战性。为应对这些挑战，我们提出一种生成文化合理句子的数据增强技术，并在乌尔都语、沙姆基语、信德语和普什图语这四种低资源巴基斯坦语言上实验。通过微调多语言掩码大型语言模型（LLMs），我们的方法在沙姆基语和普什图语的NER性能上展现显著提升，还探索了生成式LLMs在NER及少样本学习数据增强中的能力。
英文摘要：
Named Entity Recognition (NER), a fundamental task in Natural Language Processing (NLP), has shown significant advancements for high-resource languages. However, due to a lack of annotated datasets and limited representation in Pre-trained Language Models (PLMs), it remains understudied and challenging for low-resource languages. To address these challenges, we propose a data augmentation technique that generates culturally plausible sentences and experiments on four low-resource Pakistani languages; Urdu, Shahmukhi, Sindhi, and Pashto. By fine-tuning multilingual masked Large Language Models (LLMs), our approach demonstrates significant improvements in NER performance for Shahmukhi and Pashto. We further explore the capability of generative LLMs for NER and data augmentation using few-shot learning.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
