Unsupervised Data Validation Methods for Efficient Model Training

TL;DR

This paper reviews current methods and challenges in improving data quality and accessibility for training machine learning models on low-resource languages, proposing future research directions to optimize data use and model performance.

Contribution

It provides a comprehensive review of existing data validation and augmentation techniques for low-resource languages and outlines open research questions for future advancements.

Findings

Current methodologies have limitations in data quality and accessibility.

Synthetic data generation and transfer learning show promise.

Open research questions guide future improvements in low-resource NLP.

Abstract

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.