  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.
