LATTE: a joint entity and relation extraction model based on low-rank attention mechanism and residual task-aware bottleneck

LATTE is proposed, a novel joint extraction model integrating three key components: a low-rank attention mechanism that compresses irrelevant features while retaining essential semantics; a multi-head relation-aware interaction that aligns relation prototypes to capture overlapping triples; and a residual task-aware bottleneck that performs differentiated compression to stabilize decision boundaries.