Papers with co-attention module
PM2F2N: Patient Multi-view Multi-modal Feature Fusion Networks for Clinical Outcome Prediction (2022.findings-emnlp)
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| Challenge: | Existing methods focused on time series data but ignored clinical notes . fusion of multi-modal features of patients from different views is not feasible due to the time series and clinical notes data being stored as time series. |
| Approach: | They propose to combine time series and clinical notes to fuse multi-modal features of patients from different perspectives using graph neural networks. |
| Outcome: | The proposed method is superior to existing models on MIMIC-III benchmark. |
CARE: Co-Attention Network for Joint Entity and Relation Extraction (2024.lrec-main)
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| Challenge: | Existing joint entity and relation extraction methods suffer from feature confusion or inadequate interaction between the two subtasks. |
| Approach: | They propose a Co-Attention network for joint entity and relation extraction that adopts a parallel encoding strategy to learn separate representations for each subtask. |
| Outcome: | The proposed model outperforms existing models on three datasets . it uses a parallel encoding strategy to learn separate representations for each subtask . |