@inproceedings{b35ad3690a8c4f038d02a74850d2f46c,
title = "Investigating LSTM for punctuation prediction",
abstract = "We present a neural network based punctuation prediction method using Long Short-Term Memory (LSTM) network. The proposed method uses bidirectional LSTM to encode both the past and future observation as its inputs. It models the dependency between input features and output labels through multiple layers. We also empirically study the impacts of modeling the dependency between output labels. Our results show that using a deep bi-directional LSTM is able to achieve state-of-the-art performance in punctuation prediction.",
keywords = "Conditional random field, Long short term memory, Punctuation prediction, Recurrent neural network",
author = "Kaituo Xu and Lei Xie and Kaisheng Yao",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 10th International Symposium on Chinese Spoken Language Processing, ISCSLP 2016 ; Conference date: 17-10-2016 Through 20-10-2016",
year = "2017",
month = may,
day = "2",
doi = "10.1109/ISCSLP.2016.7918492",
language = "英语",
series = "Proceedings of 2016 10th International Symposium on Chinese Spoken Language Processing, ISCSLP 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
editor = "Hsin-Min Wang and Qingzhi Hou and Yuan Wei and Tan Lee and Jianguo Wei and Lei Xie and Hui Feng and Jianwu Dang and Jianwu Dang",
booktitle = "Proceedings of 2016 10th International Symposium on Chinese Spoken Language Processing, ISCSLP 2016",
}