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Recurrent networks with attention and convolutional networks for sentence representation and classification

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Abstract

In this paper, we propose a bi-attention, a multi-layer attention and an attention mechanism and convolution neural network based text representation and classification model (ACNN). The bi-attention have two attention mechanism to learn two context vectors, forward RNN with attention to learn forward context vector \(\overrightarrow {\mathbf {c}}\) and backward RNN with attention to learn backward context vector \(\overleftarrow {\mathbf {c}}\), and then concatenation \(\overrightarrow {\mathbf {c}}\) and \(\overleftarrow {\mathbf {c}}\) to get context vector c. The multi-layer attention is the stack of the bi-attention. In the ACNN, the context vector c is obtained by the bi-attention, then the convolution operation is performed on the context vector c, and the max-pooling operation is used to reduce the dimension. After max-pooling operation the text is converted to low-dimensional sentence vector m. Finally, the Softmax classifier be used for text classification. We test our model on 8 benchmarks text classification datasets, and our model achieved a better or the same performance compare with the state-of-the-art methods.

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Acknowledgements

This research was supported by the National Natural Science Foundation of China [NSFC61572005], the Fundamental Research Funds for the Central Universities [2016JBM080], and Key Projects of Science and Technology Research of Hebei Province Higher Education [ZD2-017304].

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Authors and Affiliations

  1. School of Computer and Information Technology, Beijing Jiaotong University, Beijing, 100044, China

    Tengfei Liu, Shuangyuan Yu & Baomin Xu

  2. Department of Computer Science, Beijing Jiaotong University Haibin College, Huanghua, 061199, China

    Hongfeng Yin

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  1. Tengfei Liu
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  2. Shuangyuan Yu
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  4. Hongfeng Yin
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Correspondence to Tengfei Liu.

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Liu, T., Yu, S., Xu, B. et al. Recurrent networks with attention and convolutional networks for sentence representation and classification. Appl Intell 48, 3797–3806 (2018). https://doi.org/10.1007/s10489-018-1176-4

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