A chatting method for robots based on word vectors and recurrent neural networks

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, and achieve the effect of improving reply accuracy, accuracy and performance

Active Publication Date: 2020-07-21
SOUTHWEST JIAOTONG UNIV
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AI Technical Summary

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

Method used

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  • A chatting method for robots based on word vectors and recurrent neural networks
  • A chatting method for robots based on word vectors and recurrent neural networks
  • A chatting method for robots based on word vectors and recurrent neural networks

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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 chat method based on word vectors and cyclic neural networks. The invention adopts the continuous bag-of-words model CBOW to train autonomous word vector files, calculates the similarity of sentences according to the word vectors, and retrieves the similarity with test questions The highest question sentence is compared with the preset sentence similarity threshold. If the threshold is lower than the threshold, the model that is iteratively trained by the multi-layer bidirectional network model LSTM and attention mechanism attention will be used for generative reply. If it is higher than the threshold, the maximum similarity will be output. Respond to the corresponding answer to the question. The invention solves the problem of low reply accuracy of traditional generative chat robots. The present invention can significantly improve the accuracy of the chat robot's reply, reduce the grammatical and semantic error rate of the generative chat robot's reply, and has higher interpretability compared with a single generative chat robot, and is of great importance to the research of the question answering system in the field of chatting meaning.

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 Patents(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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