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.<\/p>\n","protected":false},"excerpt":{"rendered":"
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 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