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Vietnamese Punctuation Prediction Using Deep Neural Networks

  • Thuy Pham
  • Nhu Nguyen
  • Quang Pham
  • Han Cao
  • Binh NguyenEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12011)

Abstract

Adding appropriate punctuation marks into text is an essential step in speech-to-text where such information is usually not available. While this has been extensively studied for English, there is no large-scale dataset and comprehensive study in the punctuation prediction problem for the Vietnamese language. In this paper, we collect two massive datasets and conduct a benchmark with both traditional methods and deep neural networks. We aim to publish both our data and all implementation codes to facilitate further research, not only in Vietnamese punctuation prediction but also in other related fields. Our project, including datasets and implementation details, is publicly available at https://github.com/BinhMisfit/vietnamese-punctuation-prediction.

Keywords

Punctuation prediction BiLSTM Conditional random field Attention model 

Notes

Acknowledgement

We would like to thank The National Foundation for Science and Technology Development (NAFOSTED), University of Science, Inspectorio Research Lab, and AISIA Research Lab for supporting us throughout this paper.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Thuy Pham
    • 1
  • Nhu Nguyen
    • 1
  • Quang Pham
    • 2
  • Han Cao
    • 3
  • Binh Nguyen
    • 1
    • 3
    • 4
    Email author
  1. 1.University of ScienceVietnam National University in Ho Chi Minh CityHo Chi Minh CityVietnam
  2. 2.Singapore Management UniversitySingaporeSingapore
  3. 3.Inspectorio Research LabHo Chi Minh CityVietnam
  4. 4.AISIA Research LabHo Chi Minh CityVietnam

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