Abstract：Graph neural networks(GNN) recently appears to be an effective method to model the global context representation of samples, but defected in over-smoothing when faced with the noisy few-shot text classification scenario. We propose a dual channel graph neural network to model the full context features while making full use of the label propagation mechanism. A multi-task parameter sharing mechanism is used in the dual channels to effectively constrain the graph iteration process. Compared with the baseline graph neural network, our method achieves an average improvement of 1.51% on the FewRel dataset and 11.1% improvement on the ARSC dataset.
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2021, Vol. 35 
