This paper focuses on argument component classification for transcribed
spoken classroom discussions, with the goal of automatically classifying
student utterances into claims, evidence, and warrants. We show that an
existing method for argument component classification developed for another
educationally-oriented domain performs poorly on our dataset. We then show that
feature sets from prior work on argument mining for student essays and online
dialogues can be used to improve performance considerably. We also provide a
comparison between convolutional neural networks and recurrent neural networks
when trained under different conditions to classify argument components in
classroom discussions. While neural network models are not always able to
outperform a logistic regression model, we were able to gain some useful
insights: convolutional networks are more robust than recurrent networks both
at the character and at the word level, and specificity information can help
boost performance in multi-task training.

本文研究话语识别技术应用于学生间讨论中的参考组成分类，发现已有的教育领域的分类方法表现不佳，通过引用关于学生散文和在线对话的特征集工作，可以有效地提高性能。此外，研究比较卷积神经网络和循环神经网络在不同情况下训练，得出卷积网络在字符和单词级别上都比循环网络更稳健的结论，并指出多任务训练可以提高性能。