Few-Shot Text Classification with Edge-Labeling Graph Neural Network-Based Prototypical Network
Chen Lyu, Weijie Liu, Ping Wang · 2020
In this paper, we propose a new few-shot text classification method.Compared with supervised learning methods which require a large corpus of labeled documents, our method aims to make it possible to classify unlabeled text with few labeled data.To achieve this goal, we take advantage of advanced pre-trained language model to extract the semantic features of each document.Furthermore, we utilize an edge-labeling graph neural network to implicitly models the intra-cluster similarity and the inter-cluster dissimilarity of the documents.Finally, we take the results of the graph neural network as the input of a prototypical network to classify the unlabeled texts.We verify the effectiveness of our method on a sentiment analysis dataset and a relation classification dataset and achieve the state-of-the-art performance on both tasks.