Investigating Lstm For Punctuation Prediction
2016 10TH INTERNATIONAL SYMPOSIUM ON CHINESE SPOKEN LANGUAGE PROCESSING (ISCSLP)(2016)
摘要
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.
更多查看译文
关键词
punctuation prediction, long short term memory, recurrent neural network, conditional random field
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络