Recent advances in natural language processing (NLP) have the ability to transform how classroom learning takes place. Combined with the increasing integration of technology in today's classrooms, NLP systems leveraging question answering and dialog processing techniques can serve as private tutors or participants in classroom discussions to increase student engagement and learning. To progress towards this goal, we use the classroom discourse framework of academically productive talk (APT) to learn strategies that make for the best learning experience. In this paper, we introduce a new task, called future talk move prediction (FTMP): it consists of predicting the next talk move -- an utterance strategy from APT -- given a conversation history with its corresponding talk moves. We further introduce a neural network model for this task, which outperforms multiple baselines by a large margin. Finally, we compare our model's performance on FTMP to human performance and show several similarities between the two.

中文翻译：

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老师会怎么做？预测未来的谈话动作

自然语言处理 (NLP) 的最新进展能够改变课堂学习的方式。结合当今课堂中越来越多的技术集成，利用问答和对话处理技术的 NLP 系统可以作为私人导师或课堂讨论的参与者，以增加学生的参与度和学习。为了朝着这个目标前进，我们使用学术成果演讲 (APT) 的课堂话语框架来学习有助于获得最佳学习体验的策略。在本文中，我们引入了一项新任务，称为未来谈话动作预测 (FTMP)：它包括预测下一个谈话动作——来自 APT 的话语策略——给定对话历史及其相应的谈话动作。我们进一步为这个任务引入了一个神经网络模型，这大大优于多个基线。最后，我们将我们的模型在 FTMP 上的表现与人类表现进行了比较，并展示了两者之间的一些相似之处。