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Common sense question-answering method based on question generation and convolutional neural network

A convolutional neural network and problem-solving technology, applied in the field of natural language processing, can solve problems such as missing information, poor effect, and insufficient use of rich grammatical and semantic information.

Active Publication Date: 2020-01-03
SUN YAT SEN UNIV
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Problems solved by technology

Therefore, the reasons for the poor effect are as follows: 1) The semantic relationship between the content information and the question information is not considered, and the rich grammatical and semantic information is not fully utilized; 2) Only the vector represented by the language model is considered Prefix information, no longer taking into account possible missing information covered by each word in the complete sequence

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  • Common sense question-answering method based on question generation and convolutional neural network

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[0032] The accompanying drawings are for illustrative purposes only and cannot be construed as limiting the patent;

[0033] In order to better illustrate this embodiment, some parts in the drawings will be omitted, enlarged or reduced, and do not represent the size of the actual product;

[0034] For those skilled in the art, it is understandable that some well-known structures and descriptions thereof may be omitted in the drawings.

[0035] The technical solutions of the present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0036] Such as Figure 1-2 As shown, a general knowledge question answering method based on question generation and convolutional neural network includes the following steps:

[0037] S1: Construct the content-question input sequence, pass it into the BERT language model, and then pass the encoded vector sequence into the question generation module. The question generation module learns the i...

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Abstract

The invention provides a common sense question answering method based on question generation and a convolutional neural network. According to the method, content-problems are encoded into a vector sequence through a BERT language model; the vector sequence is sent to a problem generation module, and then is sent to a shared BERT language model; a triple composed of content, questions and answers passes through a BERT language model, an output content-question-answer coding sequence is transmitted to an answer selection module and classified through a convolutional neural network, and finally,an optimal option is selected as a candidate answer selected by the model according to scores obtained by the model.

Description

technical field [0001] The present invention relates to the technical field of natural language processing, and more specifically, to a common sense question answering method based on question generation and convolutional neural networks. Background technique [0002] In recent years, with the development of big data and computer technology, question answering systems have been applied in various industries, and question answering systems have also become a key component of intelligent robots, affecting the important link of robot-human communication and interaction. Common sense question and answer is to give a behavioral content, ask questions about the possible occurrence of the content, and predict the correct answer among the answer options. This field is a research field combining artificial intelligence and natural language processing. The commonsense reasoning involved in commonsense quizzes is easy for humans, but it is a considerable challenge for machines, so we a...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/332G06N3/04G06N3/08
CPCG06F16/3329G06N3/08G06N3/045Y02D10/00
Inventor 周瑞莹梁艺阐印鉴
Owner SUN YAT SEN UNIV
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