Generating reading comprehension questions automatically based on semantic graphs
Automatic question generation is a technology in the field of artificial intelligence.Its goal is to simulate hu-man capabilities and automatically generate relevant questions based on input text.Current research on automatic ques-tion generation is mainly based on generating questions from general datasets,and there is a lack of research on ques-tion generation specifically targeting the field of education.To this end,this article focuses on the automatic generation of questions for middle school students.First,this article constructs a dataset RACE4QG specifically designed for the training needs of question generation models to meet the unique needs of the field of middle school student education.Secondly,we developed an end-to-end automatic problem generation model,which was trained on the RACE4Q dataset.In the improved"encoder-decoder"scheme,the encoder mainly adopts a two-layer bidirectional gated recurrent unit,whose input is the word embedding and answer-tagging embedding,and the hidden layer of the encoder adopts the gated self-attention mechanism to obtain the passage-answer representation,which is then fed to the decoder to generate questions.The experimental results show that the model in this paper is better than the optimal baseline model,and the three evaluation indicators BLEU-4,ROUGE-L,and METEOR are improved by 3.61,1.66,and 1.44 points,respect-ively.
semantic graphdatasetautomatic question generation modelencoderdecoderanswer tagginggraph at-tention networkgated recurrent units
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