  To better handle long-tail cases in the sequence labeling (SL) task, in this
work, we introduce graph neural networks sequence labeling (GNN-SL), which
augments the vanilla SL model output with similar tagging examples retrieved
from the whole training set. Since not all the retrieved tagging examples
benefit the model prediction, we construct a heterogeneous graph, and leverage
graph neural networks (GNNs) to transfer information between the retrieved
tagging examples and the input word sequence. The augmented node which
aggregates information from neighbors is used to do prediction. This strategy
enables the model to directly acquire similar tagging examples and improves the
general quality of predictions. We conduct a variety of experiments on three
typical sequence labeling tasks: Named Entity Recognition (NER), Part of Speech
Tagging (POS), and Chinese Word Segmentation (CWS) to show the significant
performance of our GNN-SL. Notably, GNN-SL achieves SOTA results of 96.9 (+0.2)
on PKU, 98.3 (+0.4) on CITYU, 98.5 (+0.2) on MSR, and 96.9 (+0.2) on AS for the
CWS task, and results comparable to SOTA performances on NER datasets, and POS
datasets.
