3260 papers • 126 benchmarks • 313 datasets
Continual learning for named entity recogntion
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This work proposes a unified causal framework to retrieve the causality from both new entity types and Other-Class and applies curriculum learning to mitigate the impact of label noise and introduce a self-adaptive weight for balancing the causal effects between new entities types and other-Class.
Experiments on synthetic CL datasets derived from OntoNotes and Few-NERD show that SpanKL significantly outperforms previous SoTA in many aspects, and obtains the smallest gap from CL to the upper bound revealing its high practiced value.
This paper introduces a pooled feature distillation loss that skillfully navigates the trade-off between retaining knowledge of old entity types and acquiring new ones, thereby more effectively mitigating the problem of catastrophic forgetting.
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