Long short-term memory networks for automatic generation of conversations

T. Fujita, Wenjun Bai, Changqin Quan
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引用次数: 5

Abstract

Human Machine Interface demands the communicative propriety that would be applied in various linguistic tasks. In this research, we develop an intelligent ‘chat bot’, which generates conversational sentences via recurrent neural network and its coupled memory unit, long short-term memory (LSTM). Word strings in conversations are considered as time series data. Using a single neural network model that performs a simple task of outputting the next word from the preceding word, a conversational sentence can be generated by connecting the words. In the experiment, we performed the linguistic ‘Turning Test’ to evaluate the proposed system.
自动生成对话的长短期记忆网络
人机界面需要在各种语言任务中应用的交际得体性。在这项研究中,我们开发了一个智能“聊天机器人”,它通过循环神经网络及其耦合记忆单元长短期记忆(LSTM)生成会话句子。对话中的单词字符串被视为时间序列数据。使用单个神经网络模型,执行从前一个单词输出下一个单词的简单任务,可以通过连接单词来生成会话句子。在实验中,我们进行了语言学“转向测试”来评估所提出的系统。
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