Robot chat method based on word vectors and recurrent neural network

A technology of cyclic neural network and word vector, which is applied in the field of robot chat, can solve the problem of low accuracy of robot reply, achieve the effect of improving reply accuracy, good chat experience, and improving accuracy

Active Publication Date: 2019-08-16
SOUTHWEST JIAOTONG UNIV
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Problems solved by technology

[0005] In view of the above-mentioned deficiencies in the prior art, a robot chat method based on word vectors and recurrent neural networks provided by the present invention solves the problem of low accuracy of traditional generative chat robot replies

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  • Robot chat method based on word vectors and recurrent neural network
  • Robot chat method based on word vectors and recurrent neural network
  • Robot chat method based on word vectors and recurrent neural network

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Embodiment approach

[0051] S203. Utilize the continuous bag-of-words model CBOW to carry out word vector training according to the word dictionary of the question-and-answer sentence, and obtain the word vector file QA.conv, and its implementation method is as follows:

[0052] S2031. Randomly generate a word vector matrix embedding according to the word dictionary, and initialize the word vector matrix [-1, 1]. The size of the word vector matrix embedding is (vocabulary_size, embedding_size), where vocabulary_size represents the word dictionary size , embedding_size represents the word vector dimension;

[0053] S2032. Extract the word vectors of the surrounding words of a certain central word according to the word vector matrix embedding, and solve the mean value vector of the word vectors of the surrounding words. The expression of the mean value vector avr of the word vectors of the surrounding words is as follows:

[0054]

[0055] Among them, n represents the number of unilateral words o...

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Abstract

The invention provides a robot chatting method based on word vectors and a recurrent neural network. According to the invention, the method comprises steps of adopting a continuous bag-of-words modelCBOW to train an autonomous word vector file; calculating sentence similarity according to the word vectors; retrieving a question with the highest similarity with the test question; and comparing with a preset sentence similarity threshold, performing generative reply by utilizing a multi-layer bidirectional network model LSTM and an attention mechanism attention iterative training model when thesimilarity is lower than the threshold, and outputting an answer corresponding to the maximum similarity question for reply when the similarity is higher than the threshold. The problem that a traditional generation type chatting robot is not high in reply accuracy is solved. According to the method, the reply accuracy of the chat robot can be remarkably improved, the grammatical semantic error rate of the replying of the generative chat robot is reduced, and compared with a single generative chat robot, the method has higher interpretability and has important significance for research of a question and answer system in the field of chat.

Description

technical field [0001] The invention belongs to the technical field of robot chatting, in particular to a robot chatting method based on a word vector and a recurrent neural network. Background technique [0002] With the rapid development of artificial intelligence technology, research on intelligent chat robot technology has attracted widespread attention. At present, the main applied technologies are divided into retrieval and generation. The retrieval chatbot analyzes the grammar and semantics of the sentence and searches the database for the most suitable answer sentence. Therefore, the retrieval method is more suitable for some service industries ( Such as: medicine, industry, bank), obviously, the focus of the retrieval method is on the quality of the corpus, and the quality of the corpus has become a major factor limiting its performance; Training, the most popular model at present is the seq2seq model. As long as the knowledge field involved in the corpus is wide e...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/00G06N3/04G06F16/33G06F16/332
CPCG06N3/008G06F16/3329G06F16/3344G06N3/045
Inventor 苟先太康立烨张葛祥胡梦陶明江
Owner SOUTHWEST JIAOTONG UNIV
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