Vietnamese punctuation prediction using deep neural networks

• Main Authors: PHAM, Thuy, NGUYEN, Nhu, PHAM, Hong Quang, CAO, Han, NGUYEN, Binh
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2020
• Subjects: Attention model; BiLSTM; Conditional random field; Punctuation prediction; Numerical Analysis and Scientific Computing; South and Southeast Asian Languages and Societies
• Online Access: https://ink.library.smu.edu.sg/sis_research/7817; https://ink.library.smu.edu.sg/context/sis_research/article/8820/viewcontent/VietnamesePunc_av.pdf
• Institution: Singapore Management University

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.