Few Shot Relation Extraction
Related Papers
Exploring Task Difficulty for Few-Shot Relation Extraction
AbstractFew-shot relation extraction (FSRE) focuses on recognizing novel relations by learning with merely a handful of annotated instances. Meta-learning has been widely adopted for such a task, which trains on …...
A Simple yet Effective Relation Information Guided Approach for Few-Shot Relation Extraction
AbstractFew-Shot Relation Extraction aims at predicting the relation for a pair of entities in a sentence by training with a few labelled examples in each relation. Some recent works have …...
Better Few-Shot Relation Extraction with Label Prompt Dropout
AbstractFew-shot relation extraction aims to learn to identify the relation between two entities based on very limited training examples. Recent efforts found that textual labels (i.e., relation names and relation …...
DRK: Discriminative Rule-based Knowledge for Relieving Prediction Confusions in Few-shot Relation Extraction
AbstractFew-shot relation extraction aims to identify the relation type between entities in a given text in the low-resource scenario. Albeit much progress, existing meta-learning methods still fall into prediction confusions …...
Graph-based Model Generation for Few-Shot Relation Extraction
AbstractFew-shot relation extraction (FSRE) has been a challenging problem since it only has a handful of training instances. Existing models follow a ‘one-for-all’ scheme where one general large model performs …...
Pre-training to Match for Unified Low-shot Relation Extraction
AbstractLow-shot relation extraction (RE) aims to recognize novel relations with very few or even no samples, which is critical in real scenario application. Few-shot and zero-shot RE are two representative …...