GNN-SL: Sequence Labeling Based on Nearest Examples via GNN
This addresses improved accuracy for sequence labeling tasks like NER and POS, but it is incremental as it builds on existing retrieval and GNN methods.
The paper tackles long-tail cases in sequence labeling by introducing GNN-SL, which augments a vanilla model with similar tagging examples retrieved from training data and uses a GNN to transfer information, achieving state-of-the-art results such as 96.9 (+0.2) on PKU for Chinese word segmentation.
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.