提升低资源语言模型训练效率，关键在数据质量与生成方法
Unsupervised Data Validation Methods for Efficient Model Training
- 聚焦低资源语言的数据质量问题，探索数据定义与生成策略
- 综合评估数据增强、合成数据等技术，发现现有方法仍有局限
- 为未来研究提供框架，适合关注跨语言模型与数据效率的研究者
本文探讨了在低资源语言中提升机器学习系统性能的挑战与解决方案。当前自然语言处理（NLP）、文本转语音（TTS）、语音转文字（STT）及视觉-语言模型（VLM）高度依赖大规模数据，而这些数据对低资源语言往往不可得。研究重点包括‘高质量数据’的定义、合适数据的生成方法以及模型训练的数据可及性提升。通过全面回顾数据增强、多语言迁移学习、合成数据生成和数据选择等技术，揭示了现有方法的进展与不足。论文识别出若干开放性研究问题，提出优化数据利用、减少数据需求量并保持高性能的框架，旨在使先进机器学习模型更广泛适用于低资源语言，提升其在各领域的实用价值与影响力。
原文摘要 · Abstract (English)
This paper investigates the challenges and potential solutions for improving machine learning systems for low-resource languages. State-of-the-art models in natural language processing (NLP), text-to-speech (TTS), speech-to-text (STT), and vision-language models (VLM) rely heavily on large datasets, which are often unavailable for low-resource languages. This research explores key areas such as defining "quality data," developing methods for generating appropriate data and enhancing accessibility to model training. A comprehensive review of current methodologies, including data augmentation, multilingual transfer learning, synthetic data generation, and data selection techniques, highlights both advancements and limitations. Several open research questions are identified, providing a framework for future studies aimed at optimizing data utilization, reducing the required data quantity, and maintaining high-quality model performance. By addressing these challenges, the paper aims to make advanced machine learning models more accessible for low-resource languages, enhancing their utility and impact across various sectors.
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