Papers with Computational
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. |
Analogous Process Structure Induction for Sub-event Sequence Prediction (2020.emnlp-main)
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| Challenge: | Existing work on event understanding is focusing on procedural (or horizontal) tasks such as predicting the next event given an observed sequence. |
| Approach: | They propose an Analogous Process Structure Induction framework which leverages analogies among processes and conceptualization of sub-event instances to predict the whole sub- sequence of previously unseen open-domain processes. |
| Outcome: | The proposed framework can predict the whole sub-event sequence of previously unseen open-domain processes. |