@inproceedings{gale2017experiments,
author = {Gale, William and Parthasarathy, Sarangarajan},
title = {Experiments in character-level neural network models for punctuation},
organization = {ISCA},
booktitle = {Interspeech 2017},
year = {2017},
month = {September},
abstract = {We explore character-level neural network models for inferring punctuation from text-only input. Punctuation inference is treated as a sequence tagging problem where the input is a sequence of un-punctuated characters, and the output is a corresponding sequence of punctuation tags. We experiment with six architectures, all of which use a long short-term memory (LSTM) network for sequence modeling. They differ in the way the context and lookahead for a given character is derived: from simple character embedding and delayed output to enable lookahead, to complex convolutional neural networks (CNN) to capture context. We demonstrate that the accuracy of proposed character-level models are competitive with the accuracy of a state-of-the-art word-level Conditional Random Field (CRF) baseline with carefully crafted features.},
publisher = {ISCA},
url = {http://approjects.co.za/?big=en-us/research/publication/experiments-in-character-level-neural-network-models-for-punctuation/},
}