Densely Connected Bidirectional LSTM with Applications to Sentence Classification

摘要：

　　Deep neural networks have recently been shown to achieve highly competitive performance in many computer vision tasks due to their abilities of exploring in a much larger hypothesis space.However,since most deep architectures like stacked RNNs tend to suffer from the vanishing-gradient and overfitting problems,their effects are still understudied in many NLP tasks.Inspired by this,we propose a novel multilayer RNN model called densely connected bidirectional long short-term memory(DC-Bi-LSTM)in this paper,which essentially represents each layer by the concatenation of its hidden state and all preceding layers hidden states,followed by recursively passing each layers representation to all subsequent layers.We evaluate our proposed model on five benchmark datasets of sentence classification.DC-Bi-LSTM with depth up to 20 can be successfully trained and obtain significant improvements over the traditional Bi-LSTM with the same or even fewer parameters.Moreover,our model has promising performance compared with the state-ofthe-art approaches.

关键词： Sentence classification Densely connected Stacked RNNs

作者: Zixiang Ding Rui Xia Jianfei Yu Xiang Li Jian Yang

作者单位: School of Computer Science and Engineering,Nanjing University of Science and Technology,Nanjing,Chin School of Information Systems,Singapore Management University,Singapore,Singapore

会议类型: 国际会议

会议名称: 2018自然语言处理与中文计算国际会议(NLPCC2018)

会议地点: 呼和浩特

会议语种:英文

页码: 278-287

在线出版日期: 2018-08-26（万方平台首次上网日期，不代表论文的发表时间）