Research on Man-Machine Conversation System Based on GRU seq2seq Model

Wei Huang, Xiaoyu Dong, Wenqian Shang, Weiguo Lin
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Abstract

Intelligent robot Q & A is a new focus in the field of natural language understanding and processing. In recent years, with the gradual maturity of deep learning technology, more and more research has begun to model deep neural networks for text semantics. Deep neural networks are computational models abstracted from human brain cognitive processes, closer to humans. In this paper, we use GRU deep neural network to study the generative dialogue, and implement a seq2seq model manmachine conversation system. Experiments show that the confidence of the system can reach the range of human acceptance.
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基于GRU seq2seq模型的人机对话系统研究
智能机器人问答是自然语言理解与处理领域的一个新热点。近年来,随着深度学习技术的逐渐成熟,越来越多的研究开始对深度神经网络进行文本语义建模。深度神经网络是从人类大脑认知过程中抽象出来的计算模型,更接近人类。本文利用GRU深度神经网络对生成式对话进行研究,实现了一个seq2seq模型的人机对话系统。实验表明,该系统的置信度可以达到人类可接受的范围。
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