Histobot:使用深度学习技术的问题生成系统

Docca Pranav, Badri Prasad V R
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引用次数: 0

摘要

从最近使用深度学习技术自动生成问题的研究分析中,我们检查了2022年至2023年初之间的论文,这些论文来自最近使用深度学习技术自动生成问题的研究。我们的研究是在一份调查报告之后进行的,该报告扩大了对2014年底至2019年初期间出现的AQG含量早期评估的分析。我们检查了包含的研究作品,查看了(1)问题生成框架和(2)生成方法。我们发现,当代方法经常依赖于部署基于transformer的模型和GPT-n系列的生成框架,这在分析和性能方面更有效。我们发现,问题创造最近变得流行起来,并显著改善了教育领域。然而,自动生成问题并创建必要的问题模式、结构和形式可能具有挑战性。我们的额外研究建议测试出更实用、更有效的模型和策略来自主生成问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Histobot: Question Generation System Using Deep Learning Techniques
From the analysis of recent researches of automatic question generation using deep learning techniques, we examined papers between 2022 and early 2023 from the examination of recent research on automatic question production using deep learning techniques. Our study comes after the survey report that broadens the analysis of earlier evaluations of AQG content that surfaced between late 2014 and early 2019. We examined the researched works that were included, looking at things like the (1) framework for question generation and (2) generating method. We discovered that contemporary methods fre-quently rely on generative frameworks that deploy Transformer-based models and GPT-n series, which are more efficient in terms of analysis and perfor-mances. We discovered that question creation has gained popularity recently and has significantly improved the educational field. Yet, it can be challenging to produce automatic questions and create the necessary question patterns, structures, and forms. Our additional research advises testing out more prac-tical, efficient models and strategies for autonomous question generating.
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