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        <title>Argument Component Classification for Classroom Discussions</title>
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            <title>Proceedings of the 5th Workshop on Argument Mining</title>
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    <abstract>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.</abstract>
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    <identifier type="doi">10.18653/v1/W18-5208</identifier>
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%0 Conference Proceedings
%T Argument Component Classification for Classroom Discussions
%A Lugini, Luca
%A Litman, Diane
%Y Slonim, Noam
%Y Aharonov, Ranit
%S Proceedings of the 5th Workshop on Argument Mining
%D 2018
%8 November
%I Association for Computational Linguistics
%C Brussels, Belgium
%F lugini-litman-2018-argument
%X 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.
%R 10.18653/v1/W18-5208
%U https://aclanthology.org/W18-5208
%U https://doi.org/10.18653/v1/W18-5208
%P 57-67

Markdown (Informal)

[Argument Component Classification for Classroom Discussions](https://aclanthology.org/W18-5208) (Lugini & Litman, ArgMining 2018)

• Argument Component Classification for Classroom Discussions (Lugini & Litman, ArgMining 2018)

ACL

• Luca Lugini and Diane Litman. 2018. Argument Component Classification for Classroom Discussions. In Proceedings of the 5th Workshop on Argument Mining, pages 57–67, Brussels, Belgium. Association for Computational Linguistics.