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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 39, 2022 No. 5
1368-1373

Multiple-task prototypical network for few-shot text classification

Yu Junjie
Cheng Hua
Fang Yiquan
School of Information Science & Engineering, East China University of Science & Technology, Shanghai 200237, China
DOI: 10.19734/j.issn.1001-3695.2021.11.0463

Abstract

Since the prototype network cannot make full use of samples' semantic information, it's difficult for model to fully excavate the transferable features in training data. As the result, the model underperforms when it is facing unfamiliar data in a new domain. To this end, this paper made improvements from three perspectives: model structure, encoding network, and metric network, and proposed a multiple-task prototypical network MTPN. In terms of model structure, on the basis of the prototype network's metric task, it added an auxiliary classification task to constrain the training target, which could improve the semantic feature extraction ability of the model. By using multi-task learning, model obtained a semantic representation that was more relevant to the auxiliary task. In order to improve feature transferability, this paper also proposed the LF-Transformer encoder which used hierarchical attention to fuse the underlying general encoding information. The metric network used the BiGRU-based class prototype generator to make the class prototype more representative and the distance measurement more accurate. Experiments show that MTPN achieves an accuracy of 91.62% in the sentiment classification task with few samples, which is 3.5% higher than the existing best model. For samples in new field that have not appeared in training state, by using 5 references, the model can still obtain a classification accuracy of more than 90% on query samples.

Publish Information

DOI: 10.19734/j.issn.1001-3695.2021.11.0463
Publish at: Application Research of Computers Printed Article, Vol. 39, 2022 No. 5
Section: Algorithm Research & Explore
Pages: 1368-1373
Serial Number: 1001-3695(2022)05-014-1368-06

Publish History

[2021-12-24] Accepted Paper
[2022-05-05] Printed Article

Cite This Article

于俊杰, 程华, 房一泉. 少样本文本分类的多任务原型网络 [J]. 计算机应用研究, 2022, 39 (5): 1368-1373. (Yu Junjie, Cheng Hua, Fang Yiquan. Multiple-task prototypical network for few-shot text classification [J]. Application Research of Computers, 2022, 39 (5): 1368-1373. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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