EQG-RACE: Examination-Type Question Generation
نویسندگان
چکیده
Question Generation (QG) is an essential component of the automatic intelligent tutoring systems, which aims to generate high-quality questions for facilitating reading practice and assessments. However, existing QG technologies encounter several key issues concerning biased unnatural language sources datasets are mainly obtained from Web (e.g. SQuAD). In this paper, we propose innovative Examination-type approach (EQG-RACE) exam-like based on a dataset extracted RACE. Two main strategies employed in EQG-RACE dealing with discrete answer information reasoning among long contexts. A Rough Answer Key Sentence Tagging scheme utilized enhance representations input. An Answer-guided Graph Convolutional Network (AG-GCN) designed capture structure revealing inter-sentences intra-sentence relations. Experimental results show state-of-the-art performance EQG-RACE, apparently superior baselines. addition, our work has established new prototype reshaped method, provides important benchmark related research future work. We will make data code publicly available further research.
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ژورنال
عنوان ژورنال: Proceedings of the ... AAAI Conference on Artificial Intelligence
سال: 2021
ISSN: ['2159-5399', '2374-3468']
DOI: https://doi.org/10.1609/aaai.v35i14.17553