Multi-Task Learning based Online Dialogic Instruction Detection with Pre-trained Language Models
This work addresses the problem of identifying effective instructional content for students in online education, though it is incremental as it builds on existing pre-trained models and multi-task techniques.
The paper tackles the challenge of detecting online dialogic instructions, which vary widely in quality and pedagogical style, by proposing a multi-task learning approach with pre-trained language models and contrastive loss. The method achieves superior performance on a real-world educational dataset compared to representative baselines.
In this work, we study computational approaches to detect online dialogic instructions, which are widely used to help students understand learning materials, and build effective study habits. This task is rather challenging due to the widely-varying quality and pedagogical styles of dialogic instructions. To address these challenges, we utilize pre-trained language models, and propose a multi-task paradigm which enhances the ability to distinguish instances of different classes by enlarging the margin between categories via contrastive loss. Furthermore, we design a strategy to fully exploit the misclassified examples during the training stage. Extensive experiments on a real-world online educational data set demonstrate that our approach achieves superior performance compared to representative baselines. To encourage reproducible results, we make our implementation online available at \url{https://github.com/AIED2021/multitask-dialogic-instruction}.