Data Analysis and Knowledge Discovery  2020, Vol. 4 Issue (10): 113-123    DOI: 10.11925/infotech.2096-3467.2020.0206
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Classification Model for Few-shot Texts Based on Bi-directional Long-term Attention Features
Xu Tongtong,Sun Huazhi,Ma Chunmei(),Jiang Lifen,Liu Yichen
College of Computer and Information Engineering, Tianjin Normal University, Tianjin 300387, China
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Abstract

[Objective] This paper proposes a classification model for few-shot texts, aiming to address the issues of data scarcity and low generalization performance.[Methods] First, we divided the text classification tasks into multiple subtasks based on episode training mechanism in meta-learning. Then, we proposed a Bi-directional Temporal Convolutional Network (Bi-TCN) to capture the long-term contextual information of the text in each subtask. Third, we developed a Bi-directional Long-term Attention Network (BLAN) to capture more discriminative features based on Bi-TCN and multi-head attention mechanism. Finally, we used the Neural Tensor Network to measure the correlation between query samples and support set of each subtask to finish few-shot text classification.[Results] We examined our model with the ARSC dataset. The classification accuracy of this model reached 86.80% in few-shot learning setting, which was 3.68% and 1.17% better than those of the ROBUSTTC-FSL and Induction-Network-Routing models.[Limitations] The performance of BLAN on long text is not satisfactory. [Conclusions] BLAN overcomes the issue of data scarcity and captures comprehensive text features, which effectively improves the performance of few-shot text classification.

Received: 18 March 2020      Published: 09 November 2020
 ZTFLH: TP393
Corresponding Authors: Ma Chunmei     E-mail: mcmxhd@163.com