Automatic problem solving method for application problem based on graph neural network

Abstract

The invention discloses an automatic problem solving method for an application problem based on a graph neural network, and the method comprises the steps: firstly employing a cyclic neural network tocode an inputted application problem text, constructing a numerical value unit graph and a numerical value comparison graph, and enabling the output (word-level representation) of the cyclic neural network to serve as a node feature; inputting node features and two constructed graphs into a graph neural network-based encoder together to learn graph representation features of questions, so that the final graph features can contain text relationships and size information of numerical values; using one pooling item for aggregating the graph features of different groups into one, so that the output of the graph converter is obtained; finally, using the output graph features as inputs to a tree structure-based decoder to generate a final solution expression tree. According to the method, the task performance is improved through numerical representation in rich problems, and a better problem solving effect can be achieved.

Description

technical field [0001] The invention relates to the field of computational linguistics, in particular to an automatic problem-solving method for applied problems based on a graph neural network. Background technique [0002] Solving math problems, that is, automatically answering math problems based on text descriptions, has attracted the attention of researchers since the 1960s and is an important natural language understanding task. A typical math word problem is to give a description of the problem and give a short statement about the problem with the unknown quantity. Earlier studies attempted to design automatic solvers through statistical machine learning methods and semantic parsing methods. However, these methods have poor generalization because it takes a lot of effort to design appropriate functions and expression templates. [0003] In recent years, automatic solvers based on deep learning have begun to appear. These deep learning methods can automatically obta...

AI Technical Summary

Problems solved by technology

[0004] In view of the above-mentioned deficiencies in the prior art, the present invention provides an automatic problem-solving method based on a graph neural network to solve the problem of low correctness of the problem-solving expressions generated by the existing deep learning model

Images