Multi-label Few/Zero-shot Learning with Knowledge Aggregated from Multiple Label Graphs (2020.emnlp-main)
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| Challenge: | Few/zero-shot learning is a big challenge of many classification tasks, where a classifier is required to recognise instances of classes that have very few or even no training samples. |
| Approach: | They propose a multi-graph aggregation model that fuses knowledge from multiple label graphs encoding different semantic label relationships to improve multi-label zero/few-shot document classification. |
| Outcome: | The proposed model improves on two large clinical datasets and the EU legislation dataset on few/zero-shot labels. |
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