Sequence Representation

Sequence representation learning aims to capture meaningful patterns and relationships within ordered data, enabling effective modeling of diverse sequential phenomena. Current research emphasizes developing efficient and generalizable methods, focusing on architectures like Transformers and Graph Neural Networks, often incorporating contrastive learning and hierarchical approaches to improve representation quality and scalability. These advancements have significant implications for various applications, including speech recognition, recommendation systems, risk prediction, and natural language processing tasks like coreference resolution, by enabling more accurate and efficient modeling of sequential data.