Question and answer matching method based on adaptive fusion multi-attention network

A matching method and attention technology, applied in neural learning methods, biological neural network models, character and pattern recognition, etc., to achieve the effect of improving network performance
CN112966499APending Publication Date: 2021-06-15SUN YAT SEN UNIV
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Patent Information

Authority / Receiving Office
CN Β· China
Current Assignee / Owner
SUN YAT SEN UNIV
Publication Date
2021-06-15

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Abstract

The invention provides a question and answer matching method based on an adaptive fusion multi-attention network, and the method comprises the following steps: S1, converting each word in a question and an answer into a word vector, and coding the word vectors to obtain a word vector sequence; s2, extracting information of different aspects in a problem by using a plurality of self-attention networks, and respectively encoding the information into different problem vectors; s3, generating a corresponding answer vector for each question vector; and S4, calculating a matching degree score, adaptively fusing the matching degree scores of the information in multiple aspects, and carrying out question and answer matching according to the fused matching degree score. The invention provides a question and answer matching method based on an adaptive fusion multi-attention network, and solves the problem that a model using a plurality of self-attention networks at present enhances understanding of questions and answers through multi-round processing instead of obtaining multi-angle and multi-aspect information of the questions and answers.
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Description

technical field

[0001] The present invention relates to the technical field of natural language processing, and more specifically, relates to a question and answer matching method based on an adaptive fusion multi-attention network. Background technique

[0002] Deep learning has a strong function fitting ability, and using deep learning to perform answer selection tasks has the advantages of fast running speed, easy calculation, and better than traditional effects. By carefully designing the network structure and simulating the thinking process of people when choosing answers, it is expected that good results can be obtained. From previous studies, we know that the learning of question representation plays a very important role in answer selection. A good answer selection model should be able to generate high-quality question vectors and answer vectors, and fully capture the interactive relationship between questions and answers. In fact, it is difficult for a simple self...

Claims

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