Few-Shot Semantic Dependency Parsing via Graph Contrastive Learning (2024.lrec-main)
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| Challenge: | Existing graph neural networks (GNNs) have shown promising performance on semantic dependency parsing (SDP) training a high-performing model requires a large amount of labeled data and it is prone to over-fitting in the absence of sufficient labele . |
| Approach: | They propose a syntax-guided graph contrastive learning framework to train GNNs with unlabeled data and fine-tune pre-trained GNN models with few-shot labeled SDP data. |
| Outcome: | The proposed framework achieves promising results when few-shot training samples are available. |
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