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

• URL: http://arxiv.org/abs/2010.07459v1
• Date: Thu, 15 Oct 2020 01:15:43 GMT
• Title: Multi-label Few/Zero-shot Learning with Knowledge Aggregated from Multiple Label Graphs
• Authors: Jueqing Lu, Lan Du, Ming Liu, Joanna Dipnall
• Abstract summary: We present a simple multi-graph aggregation model that fuses knowledge from multiple label graphs encoding different semantic label relationships. We show that methods equipped with the multi-graph knowledge aggregation achieve significant performance improvement across almost all the measures on few/zero-shot labels.
• Score: 8.44680447457879
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 multi-label 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.

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