Self-Distillation with Meta Learning for Knowledge Graph Completion (2022.findings-emnlp)
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| Challenge: | Existing knowledge graph completion frameworks for knowledge graphs are far from complete and require missing triples to be added to them. |
| Approach: | They propose a dynamic pruning technique to obtain a pruned model from a large source model, where the pruning mask of the pruned models could be updated adaptively per epoch after the model weights are updated. |
| Outcome: | The proposed framework achieves competitive performance compared to strong baselines, while being 10x smaller than baselines. |
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