Metric learning learns a semantic metric on existing data and classifies queries by comparing them with training examples.

Hamilton College

Real-world relation distributions are long-tail: Only the common relations obtain sufficient training instances and most relations have very limited relational facts and corresponding sentences. Many relations even have less than 10 training instances. The general idea of few-shot models is to train good representations of instances or learn ways of fast adaptation from existing large-scale data, and then transfer to new tasks. 

Performing More Efficient Learning: Methods

Han, X. (n.d.). More Data, More Relations, More Context and More Openness:
A Review and Outlook for Relation Extraction *, Retrieved June 4, 2022, from https://arxiv.org/pdf/2004.03186.pdf

More Relations, More Context and More Openness:
A Review and Outlook for Relation Extraction

Metric Learning

Meta-learning, also known as “learning to learn”,  aims to learn better learning algorithms, including better parameter initialization, optimization strategy, network architecture, distance metrics.

Learn Before

Related