Papers by Deliang Wang

1 papers
On the Interpretability of Deep Learning Models for Collaborative Argumentation Analysis in Classrooms (2024.acl-srw)

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Challenge: Existing models for collaborative argumentation lack interpretability and teachers are skeptics about their use.
Approach: They propose to use four explainable AI methods to provide models for automated analysis of argument moves and specificity levels within collaborative argumentation to cultivate trust among teachers.
Outcome: The proposed models perform exceptionally well in analyzing word contributions and demonstrating that the models can be explained by a user-interface.

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