Papers with prototypical networks
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. |
Few-Shot Event Argument Extraction Based on a Meta-Learning Approach (2024.naacl-srw)
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| Challenge: | Recent studies on few-shot event extraction focus on event trigger detection and argument extraction in meta-learning contexts. |
| Approach: | They propose to use prototypical networks to perform few-shot event argument extraction . they propose to inject syntactic knowledge into the model to enhance relation embeddings . |
| Outcome: | The proposed approach achieves strong performance on ACE 2005 in several few-shot configurations. |
Enhancing the Prototype Network with Local-to-Global Optimization for Few-Shot Relation Extraction (2025.findings-naacl)
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| Challenge: | Relation Extraction (RE) is a task that aims to extract semantic relationships from unstructured text. |
| Approach: | They propose a local optimization strategy that indirectly optimizes the prototypical networks by optimizing the other information contained within the prototypes. |
| Outcome: | The proposed model improves on the FewRel 1.0 and FewRela 2.0 datasets. |
Few-shot Named Entity Recognition with Entity-level Prototypical Network Enhanced by Dispersedly Distributed Prototypes (2022.coling-1)
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| Challenge: | Existing prototypical networks for named entity recognition suffer from label dependency and tightly distributed prototypes, thus causing misclassifications. |
| Approach: | They propose an Entity-level Prototypical Network enhanced by dispersedly distributed prototypes to build entity-level prototypes and distribute them dispersionally. |
| Outcome: | The proposed system outperforms the previous models on two evaluation tasks and the Few-NERD settings in terms of overall performance. |
Multi-Level Matching and Aggregation Network for Few-Shot Relation Classification (P19-1)
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| Challenge: | Existing methods for few-shot relation classification use supervised training, but lack of large-scale manually labeled data. |
| Approach: | They propose a multi-level matching and aggregation network (MLMAN) for few-shot relation classification. |
| Outcome: | The proposed model achieves state-of-the-art performance on the FewRel dataset. |
Memorisation versus Generalisation in Pre-trained Language Models (2022.acl-long)
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| Challenge: | State-of-the-art pre-trained language models have been shown to memorise facts and perform well with limited amounts of training data. |
| Approach: | They propose to extend pre-trained language models to generalise and memorise facts in noisy and low-resource scenarios. |
| Outcome: | The proposed extension improves performance in low-resource named entity recognition tasks. |
This Reads Like That: Deep Learning for Interpretable Natural Language Processing (2023.emnlp-main)
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| Challenge: | In this work, we explore the extension of prototypical networks to natural language processing. |
| Approach: | They propose a weighted similarity measure that enhances the similarity computation by focusing on informative dimensions of pre-trained sentence embeddings. |
| Outcome: | The proposed method improves predictive performance on AG News and RT Polarity datasets and the rationale-based recurrent convolutions. |
Prompt-based Zero-shot Relation Extraction with Semantic Knowledge Augmentation (2024.lrec-main)
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| Challenge: | Existing approaches to recognize unseen relations for which there are no training instances are lacking in the real-world setting. |
| Approach: | They propose a prompt-based model with semantic knowledge augmentation to recognize unseen relations under zero-shot setting. |
| Outcome: | The proposed model outperforms existing methods under zero-shot setting on three datasets. |