Metric Learning for Dynamic Text Classification
(a) SENT
['Model', '5', '10', '20', '100']
The fine-tuned model uses this data for both additional training and for constructing new prototypes. The un-tuned model constructs prototypes using the pretrained model’s representations without additional training. We also construct an un-tuned MLP baseline by fitting a nearest neighbor classifier (KNN, k=5) on the encodings of the penultimate layer of the network. We experimented with fitting the KNN on the output predictions but found that using the penultimate layer was more effective.