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To better handle long-tail cases in the sequence labeling (SL) task, in thiswork, we introduce graph neural networks sequence labeling (GNN-SL), whichaugments the vanilla SL model output with similar tagging examples retrievedfrom the whole training set. Since not all the retrieved tagging examplesbenefit the model prediction, we construct a heterogeneous graph, and leveragegraph neural networks (GNNs) to transfer information between the retrievedtagging examples and the input word sequence. The augmented node whichaggregates information from neighbors is used to do prediction. This strategyenables the model to directly acquire similar tagging examples and improves thegeneral quality of predictions. We conduct a variety of experiments on threetypical sequence labeling tasks: Named Entity Recognition (NER), Part of SpeechTagging (POS), and Chinese Word Segmentation (CWS) to show the significantperformance 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 theCWS task, and results comparable to SOTA performances on NER datasets, and POSdatasets.

GNN-SL: Sequence Labeling Based on Nearest Examples via GNN