Dialogue generation method and device based on two-stage decoding, medium and computing equipment

A stage and decoder technology, applied in computing, biological neural network models, special data processing applications, etc., can solve the problems of model lack of information, such as reply, single, etc., to improve relevance and information, easy to control, have an interpretable effect
CN112988967APending Publication Date: 2021-06-18SOUTH CHINA UNIV OF TECH
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Patent Information

Authority / Receiving Office
CN ยท China
Current Assignee / Owner
SOUTH CHINA UNIV OF TECH
Publication Date
2021-06-18

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Abstract

The invention discloses a dialogue generation method and device based on two-stage decoding, a medium and computing equipment, and the method comprises the steps of dividing a dialogue reply generation process into two decoding stages, firstly inputting a dialogue context into a dialogue generation model, and mapping the dialogue context into a word embedding vector; inputting a word vector into a context self-attention encoder to obtain a feature vector of a dialogue context, inputting the feature vector into a first-stage Transformer decoder, and decoding to generate a notional word sequence; inputting the notional word sequence into a notional word sequence encoder to obtain a feature vector of the notional word sequence; and finally, inputting the context and the feature vector of the notional word sequence into a second-stage Transformer decoder, and decoding to generate a final reply. Through the two-stage decoding process, interference of the virtual words which are high in frequency but lack semantic information on the notional words is prevented, and therefore reply relevance and information amount are improved.
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Description

technical field

[0001] The present invention relates to the technical field of natural language processing, in particular to a two-stage decoding-based dialogue generation method and device, medium and computing equipment. Background technique

[0002] In recent years, with the development of deep learning technology and the emergence of a large number of dialogue data sets, it is possible to use deep learning technology to build dialogue systems for open fields, which greatly expands the application scenarios of dialogue systems.

[0003] In the field of dialogue generation in the open field, the current mainstream approach is based on an end-to-end generation framework: use an encoder to encode the dialogue context into a feature vector, and then use a decoder to decode and generate dialogue responses based on the previously generated vector. However, basic end-to-end dialogue generation models tend to generate generic, uninformative responses. Such as "OK", "I don't know...

Claims

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