Geometric question answering method and model based on deep learning and multi-modal numerical reasoning

Abstract

The invention discloses a deep learning and multi-modal numerical reasoning-based geometric question answering method and a text and image bimodal combined neural network model. The method comprises the following steps: respectively acquiring text information and image information about question contents; encoding the text information into a corresponding text hiding state to obtain text semantics, and encoding the image information into a corresponding image hiding state to obtain visual semantics; fusing and aligning the text semantics and the visual semantics to obtain an answering program; and calculating an answering result according to the operation mode of the answering program. According to the method, the answering accuracy can be increased, the processing efficiency can be improved, and the technology which has good accuracy and high practicability and can autonomously generate the code sequence of the answer through deep learning is achieved.

Description

technical field [0001] The invention relates to the technical field of intelligent education, in particular to a method for solving geometric problems based on deep learning and multimodal numerical reasoning, and a neural network model combining text and image dual modes. Background technique [0002] With the development and popularization of artificial intelligence, artificial intelligence has been applied to various industries, one of which is intelligent education. [0003] At present, one of the most commonly used applications is intelligent answering. Its operation method is that the user takes a picture of the corresponding question, and by identifying the content of the question in the picture, based on the content of the question, he searches in the large question bank built from a large number of questions to find the corresponding answer. Answer. [0004] However, the current commonly used method has the following technical problems: due to the many topics invol...

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Problems solved by technology

[0004] However, the current commonly used method has the following technical problems: due to the many topics involved, and each topic only needs to change the parameters or data, the answer method will change, and more answers will be derived. If only by identifying the image to search for the answer, Answers input by a single user can only be selected from a large number of answers, which is not conducive to students' extended learning, and the amount of data to be processed is large, which increases processing time and reduces processing efficiency. Moreover, if the questions are similar, it is easy to misselect. Reduce the accuracy of screening and affect the user experience

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