Papers with unified causal framework
Distilling Causal Effect from Miscellaneous Other-Class for Continual Named Entity Recognition (2022.emnlp-main)
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| Challenge: | Existing methods for Named Entity Recognition (NER) are not able to learn Other-Class in the same way as new entity types. |
| Approach: | They propose a unified causal framework to retrieve causality from new entity types and Other-Class. |
| Outcome: | The proposed method outperforms the state-of-the-art method on three benchmark datasets. |
Distilling Causal Effect of Data in Continual Few-shot Relation Learning (2024.lrec-main)
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| Challenge: | Existing methods for learning relational patterns from data are prone to catastrophic forgetting issues due to limited number of samples and continual training mode. |
| Approach: | They propose a unified causal framework for CFRL to restore causal effects from old data . they establish two additional causal paths from old to predictions by colliding with old data separately in the old feature space. |
| Outcome: | The proposed method is superior to existing state-of-the-art methods in CFRL task settings. |