Multi-label Few/Zero-shot Learning with Knowledge Aggregated from Multiple Label Graphs

Jueqing Lu, Lan Du, Ming Liu, Joanna Dipnall

Abstract:

Few/Zero-shot learning is a big challenge of
many classifications tasks, where a classifier
is required to recognise instances of classes
that have very few or even no training samples. It becomes more difficult in multilabel classification, where each instance is labelled with more than one class. In this paper, we present a simple multi-graph aggregation model that fuses knowledge from multiple label graphs encoding different semantic
label relationships in order to study how the
aggregated knowledge can benefit multi-label
zero/few-shot document classification. The
model utilises three kinds of semantic information, i.e., the pre-trained word embeddings, label description, and pre-defined label relations.
Experimental results derived on two large clinical datasets (i.e., MIMIC-II and MIMIC-III )
and the EU legislation dataset show that methods equipped with the multi-graph knowledge
aggregation achieve significant performance
improvement across almost all the measures
on few/zero-shot labels.