Automatic text generation using deep learning: providing large-scale support for online learning communities
Automatic text generation using deep learning: providing large-scale support for online learning communities
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DOI：
10.1080/10494820.2021.1993932
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发表时间：
2021-10
影响因子：
5.4
通讯作者：
Hanxiang Du;Wanli Xing;Bo Pei
中科院分区：
文献类型：
--
作者：
Hanxiang Du;Wanli Xing;Bo Pei
摘要参与网络社区对学生的学习积极性、持久性和学习效果都有显著的益处。然而，维护和支持在线学习社区是非常具有挑战性的，需要大量的工作。在这种情况下，自动支持是可取的。这项工作的目的是探索使用深度学习算法自动生成文本，为大型在线学习社区Scratch提供情感和社区支持。特别是，最先进的深度学习语言模型GPT-2和递归神经网络（RNN）是使用来自在线学习社区的200万条评论进行训练的。然后，我们进行了可读性测试和人工评估的自动生成的结果提供支持的在线学生。结果表明，GPT-2语言模型可以提供及时的和人类写的像答复的风格真正的数据集和上下文提供相关的支持。
ABSTRACT 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.