Semi Supervised Sequence

Semi-supervised sequence learning focuses on training models for sequential data (like text or speech) using limited labeled examples and abundant unlabeled data. Current research emphasizes techniques like knowledge distillation from large language models and consistency regularization, often employing encoder-decoder architectures such as sequence-to-sequence models and variational autoencoders. These methods aim to improve model performance in low-resource scenarios, particularly for tasks like speech recognition and natural language processing, by leveraging the information contained within unlabeled data to augment limited supervised training. This approach holds significant promise for reducing the need for extensive manual annotation, thereby accelerating progress in various fields.