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Bi-Granularity Contrastive Learning for Post-Training in Few-Shot Scene
The major paradigm of applying a pre-trained language model to downstream tasks is to fine-tune it on labeled task data, which often suffers instability and low performance when the labeled examples are scarce.~One way to alleviate this problem is to apply post-training on unlabeled task data before fine-tuning, adapting the pre-trained model to target domains by contrastive learning that considers either token-level or sequence-level similarity.
Multi-Task Regularization with Covariance Dictionary for Linear Classifiers
In this paper we propose a multi-task linear classifier learning problem called D-SVM (Dictionary SVM).
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