Year: 2026
Type: conference-paper
Abstract: Multimodal Named Entity Recognition relies on visual context to resolve textual ambiguities.To mitigate data scarcity, Data Augmentation (DA) has become a standard practice; however, existing methods predominantly adopt a one-size-fits-all and random perturbation paradigm, ignoring the internal state of the target model.In this paper, we first conduct a quantitative analysis, revealing that a significant portion of errors (over 30%) are model-specific, stemming from the unique biases of different architectures.To address this, we propose Memory-Guided Hard Data Augmentation, a framework designed to systematically repair these specific defects.First, we employ K-fold cross-validation to identify model-specific Hard Data.Second, we construct a Memory Tree and utilize Large Language Models (LLMs) with a clustering mechanism to induce macro-level error patterns from micro-level failures.This facilitates a paradigm shift from stateless instance-driven augmentation to a logical pattern-driven approach.Finally, we introduce an iterative augmentation mechanism that triggers recursive generation for stubborn instances that fail initial quality filters.Extensive experiments on Twitter-2015 and Twitter-2017 benchmarks demonstrate that our framework consistently yields significant performance gains across various MNER backbones.
Institutions: Donghua University, Huawei Technologies (China), Fudan University
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166855293