GRADUAL: Granularity-aware Dual Prototype Learning for Better Few-Shot Relation Extraction (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for few-shot relation extraction use text labels and context sentences to learn prototype representations. |
| Approach: | They propose a "dual prototype learning method" that integrates text labels and context sentences into prototype representations. |
| Outcome: | The proposed method achieves state-of-the-art performance in few-shot relation extraction. |
Similar Papers
Dependency-aware Prototype Learning for Few-shot Relation Classification (2022.coling-1)
Copied to clipboard
| Challenge: | Existing methods for few-shot relation classification fail to distinguish multiple relations that co-exist in one sentence. |
| Approach: | They propose a dependency-aware prototype learning method for few-shot relation classification . they utilize dependency trees and shortest dependency paths as structural information . |
| Outcome: | The proposed method achieves better performance than baselines on the FewRel dataset. |
Bridging Text and Knowledge with Multi-Prototype Embedding for Few-Shot Relational Triple Extraction (2020.coling-main)
Copied to clipboard
| Challenge: | Existing approaches to supervised relational triple extraction require huge amounts of labeled data. |
| Approach: | They propose a multi-prototype embedding network model to extract the composition of relational triples from unstructured text. |
| Outcome: | The proposed method improves the performance of the few-shot relational triple extraction problem. |
RAPL: A Relation-Aware Prototype Learning Approach for Few-Shot Document-Level Relation Extraction (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods to identify semantic relations between entities are time-consuming and labor-intensive. |
| Approach: | They propose a relation-aware prototype learning method for document-level relation extraction (FSDLRE) they propose RAPL, which judiciously leverages relation descriptions and real NOTA instances as guidance . |
| Outcome: | The proposed method outperforms state-of-the-art approaches by 2.61% F1 . it generates task-specific NOTA prototypes and refines relation prototypes . |
Learn from Relation Information: Towards Prototype Representation Rectification for Few-Shot Relation Extraction (2022.findings-naacl)
Copied to clipboard
| Challenge: | Existing methods for Few-shot Relation Extraction focus on implicitly introducing relation information to constrain the prototype representation learning. |
| Approach: | They propose a parameter-less method to promote few-shot relation extraction . they use a prototype rectification module to rectify original prototypes by relation information . |
| Outcome: | The proposed method achieves state-of-the-art on fewRel 1.0 and 2.0 datasets. |
Consistent Prototype Learning for Few-Shot Continual Relation Extraction (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods for few-shot continual relation extraction are overfitting memory samples, resulting in insufficient activation of old relations and limited ability to handle confusion of similar classes. |
| Approach: | They propose a few-shot continual relation extraction task that uses memory-enhanced modules to train a model on incrementally few-shot data to avoid forgetting old relations. |
| Outcome: | The proposed method outperforms existing methods on two commonly-used datasets. |
A Simple yet Effective Relation Information Guided Approach for Few-Shot Relation Extraction (2022.findings-acl)
Copied to clipboard
| Challenge: | Existing approaches to introduce relation information into the model are limited by labeling and data scarcity. |
| Approach: | They propose a direct addition approach to introduce relation information into a model by concatenating two views of relations and adding them to the original prototype. |
| Outcome: | The proposed approach improves on the benchmark dataset FewRel 1.0 and shows comparable results to the state-of-the-art. |
Learning Prototype Representations Across Few-Shot Tasks for Event Detection (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing training data for event detection are too expensive to achieve in real applications where novel event types emerge . Typical ED systems require labeled data for each predefined event type, but only a few examples are available. |
| Approach: | They propose to introduce cross-task prototypes to model relationships between training tasks in few-shot learning for event detection. |
| Outcome: | The proposed model improves on three few-shot learning datasets. |
Mitigating Non-Representative Prototypes and Representation Bias in Few-Shot Continual Relation Extraction (2025.acl-long)
Copied to clipboard
| Challenge: | Existing methods for few-shot continual relation extraction (FCRE) face two main challenges: non-representative prototypes and representation bias. |
| Approach: | They propose to use General Orthogonal Frame to create robust class prototypes . they also utilize label description representations as global class representatives . |
| Outcome: | The proposed method outperforms state-of-the-art methods on well-known benchmarks on well known FCRE benchmarks. |
Towards Realistic Few-Shot Relation Extraction (2021.emnlp-main)
Copied to clipboard
| Challenge: | Recent studies have shown that few-shot relation classification models can be used to extract any relation of interest from a collection of text with only a few example instances. |
| Approach: | They propose to modify the training routine to encourage models to better discriminate between relations involving similar entity types. |
| Outcome: | The proposed models outperform human models on relation extraction tasks while relying on entity type information. |
Improving Few-Shot Relation Classification by Prototypical Representation Learning with Definition Text (2022.findings-naacl)
Copied to clipboard
| Challenge: | Existing approaches to few-shot relation classification have limited labeled examples . a prototype encoder from definition and an instance is needed to learn relation instance classification . |
| Approach: | They propose to learn a prototype encoder from relation definition in a way that is useful for relation instance classification. |
| Outcome: | The proposed encoder outperforms state-of-the-art methods on several datasets. |