Low Resource Scenario

Low-resource scenarios in natural language processing and speech processing address the challenges of building effective models with limited training data, hindering performance in various tasks like speech-to-text, relation extraction, and question answering. Current research focuses on leveraging techniques like data augmentation (e.g., interpolation), transfer learning from high-resource languages, and adapting existing large language models (LLMs) or neural networks through methods such as self-knowledge distillation and hierarchical softmax. These efforts aim to improve model generalization and robustness in resource-constrained settings, ultimately enabling broader access to advanced language technologies across diverse languages and domains.