Automatic Question Generation for Language Learning Task Based on the Grid-Based Language Structure Parsing Framework

In the field of natural language generation in Chinese, there has been limited attention to question generation tasks, partly due to constraints related to knowledge acquisition. With the increasing popularity of online education, the automatic generation of questions has become a major point in the context of language intelligent education. In this regard, this paper is oriented towards international Chinese language education. This paper constructs a domain knowledge database and utilizes the grid-based language structure parsing framework in conjunction with the domain knowledge database to perform syntactic and semantic analysis on text. In turn, this enables the automatic generation of short-answer questions based on the results of syntactic and semantic analysis, along with pre-defined question keywords, achieving an accuracy rate as high as 94%. This not only provides a high-accuracy domain-specific research model for automatic question generation but also offers high-quality question-answer pairs for second language learning.