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Deep Attentive Structured Language Model Based on LSTM

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Intelligence Science and Big Data Engineering (IScIDE 2017)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10559))

Abstract

Language model (LM) plays an essential role in natural language processing tasks. Given the context, the language model can predict the next word. However, when the history becomes longer, the single hidden vector may be not big enough to store the entire information. In this paper, we propose a deep attentive structured language model (DAS LM), which extends the Long Short-Term Memory (LSTM) neural network with the attention mechanism. With the alternative input of part of speech (POS) tags, the language model is capable of extracting relations between a word and its context. Our model is evaluated on Penn Treebank, Chinese short message and Swb-Fisher corpora. The experiments in language modeling show that our model achieves significant improvements compared to the conventional LSTM language model.

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Notes

  1. 1.

    The LSTMN language model is implemented by us. The PPL score in the original paper is 108.

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Acknowledgement

This work was supported by the Shanghai Sailing Program No. 16YF1405300, the China NSFC projects (Nos. 61573241 and 61603252) and the Interdisciplinary Program (14JCZ03) of Shanghai Jiao Tong University in China. Experiments have been carried out on the PI supercomputer at Shanghai Jiao Tong University.

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Authors and Affiliations

  1. Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering SpeechLab, Department of Computer Science and Engineering, Brain Science and Technology Research Center, Shanghai Jiao Tong University, Shanghai, China

    Di Cao & Kai Yu

Authors
  1. Di Cao
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  2. Kai Yu
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Corresponding author

Correspondence to Kai Yu .

Editor information

Editors and Affiliations

  1. Dalian University of Technology, Dalian, China

    Yi Sun

  2. Dalian University of Technology, Dalian, China

    Huchuan Lu

  3. Dalian University of Technology, Dalian, China

    Lihe Zhang

  4. Nanjing University of Science and Technology, Nanjing, China

    Jian Yang

  5. Beijing Institute of Technology, Beijing, China

    Hua Huang

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Cao, D., Yu, K. (2017). Deep Attentive Structured Language Model Based on LSTM. In: Sun, Y., Lu, H., Zhang, L., Yang, J., Huang, H. (eds) Intelligence Science and Big Data Engineering. IScIDE 2017. Lecture Notes in Computer Science(), vol 10559. Springer, Cham. https://doi.org/10.1007/978-3-319-67777-4_15

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  • DOI: https://doi.org/10.1007/978-3-319-67777-4_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67776-7

  • Online ISBN: 978-3-319-67777-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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