Neural CRF transducers for sequence labeling
Abstract
Conditional random fields (CRFs) have been shown to be one of the most successful approaches to sequence labeling. Various linear-chain neural CRFs (NCRFs) are developed to implement the non-linear node potentials in CRFs, but still keeping the linear-chain hidden structure. In this paper, we propose NCRF transducers, which consists of two RNNs, one extracting features from observations and the other capturing (theoretically infinite) long-range dependencies between labels. Different sequence labeling methods are evaluated over POS tagging, chunking and NER (English, Dutch). Experiment results show that NCRF transducers achieve consistent improvements over linear-chain NCRFs and RNN transducers across all the four tasks, and can improve state-of-the-art results.
Keywords
Cite
@article{arxiv.1811.01382,
title = {Neural CRF transducers for sequence labeling},
author = {Kai Hu and Zhijian Ou and Min Hu and Junlan Feng},
journal= {arXiv preprint arXiv:1811.01382},
year = {2018}
}