Papers with label-wise attention
This Patient Looks Like That Patient: Prototypical Networks for Interpretable Diagnosis Prediction from Clinical Text (2022.aacl-main)
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Betty van Aken, Jens-Michalis Papaioannou, Marcel Naik, Georgios Eleftheriadis, Wolfgang Nejdl, Felix Gers, Alexander Loeser
| Challenge: | a novel method for diagnosis prediction from clinical text is needed in clinical practice . prototypical part networks and label-wise attention are used to make models interpretable and helpful . |
| Approach: | They propose a deep neural model that makes predictions based on parts of the text that are similar to prototypical patients. |
| Outcome: | The proposed method outperforms baseline models on two clinical datasets and provides valuable explanations for clinical decision support. |
Large-Scale Multi-Label Text Classification on EU Legislation (P19-1)
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| Challenge: | Large-Scale multi-label text classification is a task of assigning to each document all the relevant labels from a large set, typically containing thousands of labels (classes). |
| Approach: | They propose to use a dataset of 57k English EU legislative documents annotated with 4.3k EUROVOC labels for LMTC, few-shot learning and contextual embeddings. |
| Outcome: | The proposed dataset is suitable for LMTC, few- and zero-shot learning and bypasses the maximum text length limit. |