.png){kind=icon}
返回
Automatic text generation using deep learning: providing large-scale support for online learning communities
.png){kind=icon}
PRE
AI
DOI：10.1080/10494820.2021.1993932.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Participating in online communities has significant benefits to students learning in terms of students' motivation, persistence, and learning outcomes. However, maintaining and supporting online learning communities is very challenging and requires tremendous work. Automatic support is desirable in this situation. The purpose of this work is to explore the use of deep learning algorithms for automatic text generation in providing emotional and community support for a massive online learning community, Scratch. Particularly, state-of-art deep learning language models GPT-2 and recurrent neural network (RNN) are trained using two million comments from the online learning community. We then conduct both a readability test and human evaluation on the automatically generated results for offering support to the online students. The results show that the GPT-2 language model can provide timely and human-written like replies in a style genuine to the data set and context for offering related support.
Keyword:
Deep learning
artificial intelligence
online learning
language models
text generation
online communities
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
5.3
论文数:
2.8K
被引数:
9.0K
机构
引用论文
.png){kind=icon}
Linear parameter varying system based modeling of hemodynamic response to profiled hemodialysis基于轮廓化血液透析的血流动力学响应的线性参数变化系统建模
PREAI
分享收藏分享收藏Minimal gene selection for classification and diagnosis prediction based on gene expression profile
OAAI
OAAI分享收藏
查看更多内容