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Found 4 papers, 3 papers with code
Early Stopping Based on Unlabeled Samples in Text Classification
The proposed method is advantageous because it does not require a separate validation set and provides a better stopping point by using a large unlabeled set.
Domain Knowledge Transferring for Pre-trained Language Model via Calibrated Activation Boundary Distillation
In this study, we propose a domain knowledge transferring (DoKTra) framework for PLMs without additional in-domain pretraining.
Exploiting all samples in low-resource sentence classification: early stopping and initialization parameters
Finally, the proposed learning strategy is to train all samples with the good initialization parameters and stop the model with the early stopping techniques.
GIST at SemEval-2018 Task 12: A network transferring inference knowledge to Argument Reasoning Comprehension task
A key idea for our system is full use of transfer learning from the Natural Language Inference (NLI) task to this task.
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