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Meta-Learning for Few-Shot Named Entity Recognition
Meta-learning has recently been proposed to learn models and algorithms that can generalize from a handful of examples.
Towards Multi-Objective Statistically Fair Federated Learning
With this goal in mind, we conduct studies to show that FL is able to satisfy different fairness metrics under different data regimes consisting of different types of clients.
A Closer Look At Feature Space Data Augmentation For Few-Shot Intent Classification
In particular, we show that (a) upsampling in latent space is a competitive baseline for feature space augmentation (b) adding the difference between two examples to a new example is a simple yet effective data augmentation method.
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