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Found 8 papers, 0 papers with code
How Can I Improve? Using GPT to Highlight the Desired and Undesired Parts of Open-ended Responses
To quantify the quality of highlighted praise components identified by GPT models, we introduced a Modified Intersection over Union (M-IoU) score.
Harnessing the Power of Beta Scoring in Deep Active Learning for Multi-Label Text Classification
Within the scope of natural language processing, the domain of multi-label text classification is uniquely challenging due to its expansive and uneven label distribution.
Re-weighting Tokens: A Simple and Effective Active Learning Strategy for Named Entity Recognition
Active learning, a widely adopted technique for enhancing machine learning models in text and image classification tasks with limited annotation resources, has received relatively little attention in the domain of Named Entity Recognition (NER).
Low-Resource Named Entity Recognition: Can One-vs-All AUC Maximization Help?
Named entity recognition (NER), a task that identifies and categorizes named entities such as persons or organizations from text, is traditionally framed as a multi-class classification problem.
Using Large Language Models to Provide Explanatory Feedback to Human Tutors
Research demonstrates learners engaging in the process of producing explanations to support their reasoning, can have a positive impact on learning.
Robust Educational Dialogue Act Classifiers with Low-Resource and Imbalanced Datasets
We note that many prior studies on classifying educational DAs employ cross entropy (CE) loss to optimize DA classifiers on low-resource data with imbalanced DA distribution.
AUC Maximization for Low-Resource Named Entity Recognition
To the best of our knowledge, this is the first work that brings AUC maximization to the NER setting.
Low Resource Named Entity Recognition
named-entity-recognition
+2
Hardness-guided domain adaptation to recognise biomedical named entities under low-resource scenarios
Domain adaptation is an effective solution to data scarcity in low-resource scenarios.
