Few-Shot Text Classification with Edge-Labeling Graph Neural Network-Based Prototypical Network

Chen Lyu, Weijie Liu, Ping Wang


Abstract
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.
Anthology ID:
2020.coling-main.485
Volume:
Proceedings of the 28th International Conference on Computational Linguistics
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
5547–5552
Language:
URL:
https://aclanthology.org/2020.coling-main.485
DOI:
10.18653/v1/2020.coling-main.485
Bibkey:
Cite (ACL):
Chen Lyu, Weijie Liu, and Ping Wang. 2020. Few-Shot Text Classification with Edge-Labeling Graph Neural Network-Based Prototypical Network. In Proceedings of the 28th International Conference on Computational Linguistics, pages 5547–5552, Barcelona, Spain (Online). International Committee on Computational Linguistics.
Cite (Informal):
Few-Shot Text Classification with Edge-Labeling Graph Neural Network-Based Prototypical Network (Lyu et al., COLING 2020)
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PDF:
https://aclanthology.org/2020.coling-main.485.pdf