Papers with Student
Self-training Improves Pre-training for Few-shot Learning in Task-oriented Dialog Systems (2021.emnlp-main)
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| Challenge: | Large-scale pre-trained language models have shown promising results for few-shot learning in task-oriented dialog (ToD) systems. |
| Approach: | They propose a self-training approach that iteratively labels the most confident unlabeled data to train a stronger Student model. |
| Outcome: | The proposed approach improves state-of-the-art pre-trained models in few-shot learning scenarios for task-oriented dialog (ToD) systems when only a small number of labeled data are available. |
Can you Summarize my learnings? Towards Perspective-based Educational Dialogue Summarization (2023.findings-emnlp)
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| Challenge: | Increasing use of virtual tutors has allowed for more efficient, personalized, and interactive AI-based learning experiences. |
| Approach: | They propose a task of Multi-modal Perspective based Dialogue Summarization (MM-PerSumm) that summarizes educational dialogues from three unique perspectives: the Student, the Tutor, and a Generic viewpoint. |
| Outcome: | The proposed model can summarize educational dialogues from three perspectives, while student-oriented summaries should distill learning points, track progress, and suggest scope for improvement. |
MalruleLib: Large-Scale Executable Misconception Reasoning with Step Traces for Modeling Student Thinking in Mathematics (2026.acl-long)
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| Challenge: | MalruleLib is a learning-science-grounded framework that translates documented misconceptions into executable procedures and generates step-by-step traces of malrule-consistent student reasoning. |
| Approach: | They propose a learning-science-grounded framework that translates documented misconceptions into executable procedures and generates step-by-step traces of malrule-consistent student reasoning. |
| Outcome: | The framework translates misconceptions into executable procedures and generates step-by-step traces of malrule-consistent student reasoning. |