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A Scalable Attention Mechanism Based Neural Network for Text Classification by Zheng, Jianyun; Pang, Jianmin; Zhang, Xiaochuan; Sun, Di; Zhou, Xin; Zhang, Kai; Wang, Dong; Li, MingLiang; Wang, Jun is a Physics and Astronomy article available to read on EtoBox.
What is A Scalable Attention Mechanism Based Neural Network for Text Classification about?
## Abstract In general, deep learning based text classification methods are considered to be effective but tend to be relatively slow especially for model training. In this work, we present a powerful, so-called “scalable attention mechanism”, which performs better than conventional attention mechanism in terms of both effectiveness and the speed of model training. Based on the scalable attention mechanism, we propose a neural network for text classification. The experimental results on eight representative datasets show that our method can obtain similar accuracy to state-of-the-art methods with training in less than 4 minutes on an NVIDIA GTX 1080Ti GPU. To the best of our knowledge, our method is at least twice faster than all the published deep learning classifiers.
Who reads A Scalable Attention Mechanism Based Neural Network for Text Classification?
It is typically read by researchers, students, and practitioners in Physics and Astronomy.
- Author
- Zheng, Jianyun; Pang, Jianmin; Zhang, Xiaochuan; Sun, Di; Zhou, Xin; Zhang, Kai; Wang, Dong; Li, MingLiang; Wang, Jun
- Publisher
- Institute of Physics; IOP Publishing (ISSN 1742-6588)
- Published
- 2020
- Language
- EN
- Field
- Physics and Astronomy (Physical Sciences)