@inproceedings{lyu-etal-2020-shot,
title = "Few-Shot Text Classification with Edge-Labeling Graph Neural Network-Based Prototypical Network",
author = "Lyu, Chen and
Liu, Weijie and
Wang, Ping",
editor = "Scott, Donia and
Bel, Nuria and
Zong, Chengqing",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.485",
doi = "10.18653/v1/2020.coling-main.485",
pages = "5547--5552",
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.",
}
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%0 Conference Proceedings
%T Few-Shot Text Classification with Edge-Labeling Graph Neural Network-Based Prototypical Network
%A Lyu, Chen
%A Liu, Weijie
%A Wang, Ping
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F lyu-etal-2020-shot
%X 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.
%R 10.18653/v1/2020.coling-main.485
%U https://aclanthology.org/2020.coling-main.485
%U https://doi.org/10.18653/v1/2020.coling-main.485
%P 5547-5552
Markdown (Informal)
[Few-Shot Text Classification with Edge-Labeling Graph Neural Network-Based Prototypical Network](https://aclanthology.org/2020.coling-main.485) (Lyu et al., COLING 2020)
ACL