An Effective Incorporating Heterogeneous Knowledge Curriculum Learning for Sequence Labeling

arXiv:2402.13534v1 Announce Type: new
Abstract: Sequence labeling models often benefit from incorporating external knowledge. However, this practice introduces data heterogeneity and complicates the model with additional modules, leading to increased expenses for training a high-performing model. To address this challenge, we propose a two-stage curriculum learning (TCL) framework specifically designed for sequence labeling tasks. The TCL framework enhances training by gradually introducing data instances from easy to hard, aiming to improve both performance and training speed. Furthermore, we explore different …

abstract arxiv benefit challenge cs.ai cs.cl curriculum curriculum learning data framework knowledge labeling modules practice stage tcl training type