Papers with FewRel
RelationPrompt: Leveraging Prompts to Generate Synthetic Data for Zero-Shot Relation Triplet Extraction (2022.findings-acl)
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| Challenge: | Existing approaches to extract relation triplets require large datasets and a fixed set of relations. |
| Approach: | They propose to use a sentence-based task setting to generalize relation extraction methods to unseen relation sets. |
| Outcome: | The proposed method can extract multiple relation triplets in a sentence using language model prompts and structured text approaches. |
Zero-Shot Information Extraction as a Unified Text-to-Triple Translation (2021.emnlp-main)
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| Challenge: | a number of information extraction tasks require task-specific training. |
| Approach: | They propose a text-to-triple translation framework for information extraction tasks . they propose enabling task-agnostic translation by leveraging latent knowledge of a pre-trained language model . |
| Outcome: | The proposed framework outperforms the existing methods on open information extraction tasks. |
AlignRE: An Encoding and Semantic Alignment Approach for Zero-Shot Relation Extraction (2024.findings-acl)
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| Challenge: | Existing prototype-based methods for ZSRE ignore abundant side information and suffer from a significant encoding gap between prototypes and sentences. |
| Approach: | They propose a framework to encode schema alignment to enhance prototype-based ZSRE methods. |
| Outcome: | The proposed method outperforms existing methods on FewRel and Wiki-ZSL datasets and exhibits substantially faster performance and reduces the need for extensive manual labor in prototype construction. |
Weakly-Supervised Questions for Zero-Shot Relation Extraction (2023.eacl-main)
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| Challenge: | Zero-Shot Relation Extraction (ZRE) is a task where the training and test sets have no shared relation types. |
| Approach: | They propose to learn a model that can translate relation descriptions into relevant questions, which are then leveraged to generate the correct tail entity. |
| Outcome: | The proposed model outperforms the state-of-the-art on the fewrel and WikiZSL datasets by more than 16 F1 points without using gold question templates. |
Consistent Representation Learning for Continual Relation Extraction (2022.findings-acl)
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| Challenge: | Existing methods to train relation extraction models overfit memory samples and perform poorly on imbalanced datasets. |
| Approach: | They propose a method which uses contrastive learning and knowledge distillation to train a model on data with new relations while avoiding forgetting old ones. |
| Outcome: | The proposed method significantly outperforms state-of-the-art baselines and yields strong robustness on the imbalanced datasets. |
Matching the Blanks: Distributional Similarity for Relation Learning (P19-1)
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| Challenge: | Efforts to build general purpose relation extractors that can model arbitrary relations are limited in their ability to generalize. |
| Approach: | They propose to build task-agnostic relation representations solely from entity-linked text to extend Harris’ distributional hypothesis to relations. |
| Outcome: | The proposed representations outperform previous methods on SemEval 2010 Task 8, KBP37, and TACRED even without using any of the task’s training data. |
DSP: Discriminative Soft Prompts for Zero-Shot Entity and Relation Extraction (2023.findings-acl)
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| Challenge: | Prompt-based methods have shown their efficacy in transferring general knowledge within pre-trained language models (PLMs) however, when applied to zero-shot entity and relation extraction, they struggle with the limited coverage of verbalizers to labels and the slow inference speed. |
| Approach: | They propose a method which reformulates zero-shot tasks into token discrimination tasks without having to construct verbalizers. |
| Outcome: | The proposed method outperforms baselines on two zero-shot entity recognition datasets with higher inference speed and achieves 7.5% improvement over previous state-of-the-art models on Wiki-ZSL and FewRel. |
GLiREL - Generalist Model for Zero-Shot Relation Extraction (2025.naacl-long)
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| Challenge: | Existing approaches to zero-shot named entity recognition rely on distant supervision and training data for unseen labels. |
| Approach: | They propose an efficient architecture and training paradigm for zero-shot relation classification . they use a protocol to generate multiple relation labels in a single forward pass . |
| Outcome: | The proposed architecture and training paradigm achieve state-of-the-art results on the zero-shot relation classification task. |
A Two-Agent Game for Zero-shot Relation Triplet Extraction (2024.findings-acl)
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| Challenge: | Existing methods for relation triplet extraction rely on labeled data and are limited in their applicability. |
| Approach: | They propose a two-agent game approach to deliberate and debate unseen relations by two agents, a generator and an extractor. |
| Outcome: | The proposed method outperforms baseline methods by 6%-16% in F1 scores. |
A Spectral Viewpoint on Continual Relation Extraction (2023.findings-emnlp)
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| Challenge: | Existing methods to solve the Continual Relation Extraction problem have been proposed . |
| Approach: | They propose a class-wise regularization method that preserves eigenvectors for each class shape . they propose spectral regularization to preserve eenvector shape after learning new tasks . |
| Outcome: | The proposed method improves performance on two benchmark datasets. |
Improving Continual Few-shot Relation Extraction through Relational Knowledge Distillation and Prototype Augmentation (2024.lrec-main)
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| Challenge: | Existing approaches to Continual Relation Extraction (CRE) are limited in handling the rapid emergence of new relations in real-world scenarios. |
| Approach: | They propose a framework that integrates prototype-based data augmentation and relational knowledge distillation to solve the problem of Continual Few-shot Relation Extraction (CFRE). |
| Outcome: | The proposed framework outperforms the state-of-the-art methods on the FewRel and TACRED datasets. |