Sequence Labeling Task

Sequence labeling is a fundamental natural language processing task aiming to assign labels to sequential data, such as words in a sentence or tokens in a document, to extract structured information like parts-of-speech or named entities. Current research emphasizes improving model robustness and efficiency through techniques like curriculum learning, data augmentation (including novel methods like SegMix), and prompt engineering, often leveraging large language models (LLMs) and graph neural networks (GNNs). These advancements are crucial for enhancing performance in various applications, including information extraction from diverse data sources (e.g., multimodal conversations, visually-rich documents) and improving low-resource language processing.