SSDAU: Structured Semantic Data Augmentation for Joint Entity and Relation Extraction
Data augmentation remains a critical bottleneck in training extraction models on noisy or limited datasets. This paper addresses a real pain point: existing augmentation techniques often corrupt semantic relationships when generating synthetic training examples, degrading downstream performance. SSDAU preserves entity-relation structure by segmenting text around labeled entities and using context-aware encoding to restructure semantic content during augmentation. For practitioners building information extraction systems across domains, this approach could reduce the manual labeling burden and improve cross-domain generalization without sacrificing data quality. The work signals ongoing maturation in data-centric AI practices.
Modelwire context
ExplainerSSDAU's key contribution is preserving entity-relation dependencies during augmentation by treating labeled entities as structural anchors rather than tokens to be freely manipulated. Most prior work treats augmentation as a black-box transformation; this paper makes structure explicit.
This connects directly to the broader shift toward data-centric AI practices we've tracked. The ARES paper from last week showed how automating rubric synthesis reduces annotation overhead; SSDAU tackles the complementary problem of making existing labeled data go further without quality degradation. Both assume the bottleneck is not model capacity but the cost and fragility of training signal. The Structure-Guided Entity Resolution work also relies on preserving semantic structure during fine-tuning, suggesting practitioners across compliance and extraction tasks are converging on the same insight: rigid adherence to raw text during transformation breaks downstream performance.
If SSDAU shows comparable or better cross-domain transfer than standard augmentation on at least two out-of-domain relation extraction benchmarks (e.g., SemEval to ACE or vice versa) within the next six months, the structural preservation hypothesis holds. If gains flatten on noisy real-world datasets where entity boundaries themselves are ambiguous, the approach's practical ceiling becomes clear.
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MentionsSSDAU · Joint Entity and Relation Extraction
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