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Investigating LSTM for punctuation prediction | IEEE Conference Publication | IEEE Xplore

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 th...Show More

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.
Date of Conference: 17-20 October 2016
Date Added to IEEE Xplore: 04 May 2017
ISBN Information:
Conference Location: Tianjin, China

References

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