An Effective Incorporating Heterogeneous Knowledge Curriculum Learning for Sequence Labeling
Xuemei Tang, Jun Wang, Qi Su, Chu-Ren Huang, Jinghang Gu
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 dual-stage curriculum learning (DCL) framework specifically designed for sequence labeling tasks. The DCL framework enhances training by gradually introducing data instances from easy to hard. Additionally, we introduce a dynamic metric for evaluating the difficulty levels of sequence labeling tasks. Experiments on several sequence labeling datasets show that our model enhances performance and accelerates training, mitigating the slow training issue of complex models.
BibTeX
@inproceedings{tang-etal-2025-effective,
title = "An Effective Incorporating Heterogeneous Knowledge Curriculum Learning for Sequence Labeling",
author = "Tang, Xuemei and
Wang, Jun and
Su, Qi and
Huang, Chu-Ren and
Gu, Jinghang",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-short.38/",
doi = "10.18653/v1/2025.acl-short.38",
pages = "495--503",
ISBN = "979-8-89176-252-7"
}