Metric learning learns a semantic metric on existing data and classifies queries by comparing them with training examples. Most metric learning models perform distance measurement on sentence-level representation, but it is also possible to utilize token-level attention for finer-grained comparison.

Columbia University

There are mainly two ways for handling few-shot learning for this purpose:
- metric learning
- meta learning

Ways for handling few-shot learning for RE

Metric learning for few-shot learning

Meta learning aims to grasp the way of parameter initialization and optimization through the experience grain on the meta-train data. 


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