Human-Machine Conversation Based on Hybrid Neural Network

With the rise and popular of artificial intelligence,the technology of conversation between human and machine get more and more attention. Using neural network model on the Encoder-Decoder framework has been wildly used in translation and human-machine conversation. This paper we propose a new hybrid neural network model (HNN) which consists of some essential neural network models (that is RNN, LSTM, and CNN). At the same time, according to the number of words that each sentence contains, we will get three sub-datasets from the original dataset. Then training and testing our models on different sub-datasets. Experimental results show that the best accuracy belongs to different hybrid models on different sub-datasets, which indicate the proposed approach can make use of each models advantages aim at different sub-dataset.

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