Neural-Hidden-CRF: A Robust Weakly-Supervised Sequence Labeler
Abstract
We propose a neuralized undirected graphical model called Neural-Hidden-CRF to solve the weakly-supervised sequence labeling problem. Under the umbrella of probabilistic undirected graph theory, the proposed Neural-Hidden-CRF embedded with a hidden CRF layer models the variables of word sequence, latent ground truth sequence, and weak label sequence with the global perspective that undirected graphical models particularly enjoy. In Neural-Hidden-CRF, we can capitalize on the powerful language model BERT or other deep models to provide rich contextual semantic knowledge to the latent ground truth sequence, and use the hidden CRF layer to capture the internal label dependencies. Neural-Hidden-CRF is conceptually simple and empirically powerful. It obtains new state-of-the-art results on one crowdsourcing benchmark and three weak-supervision benchmarks, including outperforming the recent advanced model CHMM by 2.80 F1 points and 2.23 F1 points in average generalization and inference performance, respectively.
Cite
@article{arxiv.2309.05086,
title = {Neural-Hidden-CRF: A Robust Weakly-Supervised Sequence Labeler},
author = {Zhijun Chen and Hailong Sun and Wanhao Zhang and Chunyi Xu and Qianren Mao and Pengpeng Chen},
journal= {arXiv preprint arXiv:2309.05086},
year = {2023}
}
Comments
13 pages, 4 figures, accepted by SIGKDD-2023