Unsupervised Data Validation Methods for Efficient Model Training

Understand

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.