Semantic Anchor

Semantic anchors are intermediate representations used to improve the performance and interpretability of various machine learning models, particularly in tasks involving the integration of structured and unstructured data. Current research focuses on leveraging semantic anchors within contrastive learning frameworks and hierarchical decoder networks to enhance knowledge graph completion, zero-shot image retrieval, and natural language processing tasks like semantic parsing and database querying. This work aims to address limitations in existing models, such as a lack of interpretability and the inability to effectively combine structural and semantic information, leading to more accurate and explainable AI systems.