[1]XU Jian.Generating reading comprehension questions automatically based on semantic graphs[J].CAAI Transactions on Intelligent Systems,2024,19(2):420-428.[doi:10.11992/tis.202207001]
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Generating reading comprehension questions automatically based on semantic graphs

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 19 Number of periods: 2024 2 Page number: 420-428 Column: 学术论文—自然语言处理与理解 Public date: 2024-03-05
Title:
Generating reading comprehension questions automatically based on semantic graphs
Author(s):
XU Jian1; 2
1. Key Laboratory of Educational Informatization for Nationalities, Yunnan Normal University, Kunming 650500, China;
2. School of Information Engineering, Qujing Normal University, Qujing 655011, China
Keywords:
semantic graph; dataset; automatic question generation model; encoder; decoder; answer tagging; graph attention network; gated recurrent units
CLC:
TP311
DOI:
10.11992/tis.202207001
Abstract:
Automatic question generation is a technology in the field of artificial intelligence. Its goal is to simulate human capabilities and automatically generate relevant questions based on input text. Current research on automatic question generation is mainly based on generating questions from general datasets, and there is a lack of research on question 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, respectively.
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