Papers with semi-supervised EA method

1 papers
MixTEA: Semi-supervised Entity Alignment with Mixture Teaching (2023.findings-emnlp)

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Challenge: Existing methods to learn informative entity embeddings are insufficient for semi-supervised entity alignment.
Approach: They propose a semi-supervised method which guides the model learning with an end-to-end mixture teaching of manually labeled mappings and probabilistic pseudo mappings.
Outcome: The proposed method is superior to existing methods on benchmark datasets and further analyses.

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