Towards Realistic Low-resource Relation Extraction: A Benchmark with Empirical Baseline Study (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing approaches to extract relational facts from text are limited in their ability to learn from limited labeled data. |
| Approach: | They propose to use prompt-based methods with few-shot labeled data to evaluate performance . data augmentation technologies and self-training are also proposed to generate more labeles in-domain data. |
| Outcome: | The proposed methods perform well in low-resource settings with 8 relation extraction datasets. |
Similar Papers
Grasping the Essentials: Tailoring Large Language Models for Zero-Shot Relation Extraction (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing Relation extraction models require extensive annotated training data, which is costly and labor-intensive to collect. |
| Approach: | They propose a new zero-shot RE task where only relation definitions are provided instead of seen-unseen relation instances. |
| Outcome: | The proposed task significantly improves cost-effective zero-shot performance by large margins. |
Improving Data Annotation for Low-Resource Relation Extraction with Logical Rule-Augmented Collaborative Language Models (2025.naacl-long)
Copied to clipboard
| Challenge: | Low-resource relation extraction aims to identify semantic relationships using scarce labeled data. |
| Approach: | They propose a framework that iteratively integrates high-confidence predictions of rule-enhanced relation extractors with varying scales to obtain reliable pseudo annotations from massive unlabeled samples without human supervision. |
| Outcome: | The proposed framework achieves state-of-the-art on benchmark datasets in few-shot scenarios. |
MapRE: An Effective Semantic Mapping Approach for Low-resource Relation Extraction (2021.emnlp-main)
Copied to clipboard
| Challenge: | Neural relation extraction models have shown promising results on long-tail tasks, but performance drops dramatically as the number of instances for a relation decreases. |
| Approach: | They propose a framework considering both label-agnostic and label-aligned mapping information for low resource relation extraction. |
| Outcome: | The proposed framework improves on low-resource relation extraction tasks by incorporating label-agnostic and label-based mapping information in pretraining and fine-tuning. |
CrossRE: A Cross-Domain Dataset for Relation Extraction (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Relation Extraction (RE) evaluation is limited to in-domain setups . despite the drought of research on cross-domain RE, its practical importance remains . |
| Approach: | They propose a cross-domain benchmark for relation extraction which includes multi-label annotations and meta-data to include explanations and flags of difficult instances. |
| Outcome: | The proposed model includes explanations and flags of difficult instances. |
A Survey on Recent Approaches for Natural Language Processing in Low-Resource Scenarios (2021.naacl-main)
Copied to clipboard
| Challenge: | a growing body of work is focused on improving performance in low-resource settings . a goal of this study is to explain how these methods differ in their requirements . |
| Approach: | They propose to analyze data-lean scenarios across different dimensions of data availability to understand which approaches are effective in a specific low-resource setting. |
| Outcome: | The proposed methods enable learning when training data is sparse. |
Gradient Imitation Reinforcement Learning for Low Resource Relation Extraction (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods to extract relation facts from limited labeled corpora are laborintensive to obtain . Existing approaches use self-training to generate pseudo labels that will cause gradual drift problem or leverage meta-learning scheme which does not solicit feedback explicitly. |
| Approach: | They propose a Gradient Imitation Reinforcement Learning method to encourage pseudo label data to imitate gradient descent direction on labeled data and bootstrap its optimization capability through trial and error. |
| Outcome: | The proposed method handles two major scenarios in low-resource relation extraction when no unlabeled data is available. |
The State of Relation Extraction Data Quality: Is Bigger Always Better? (2024.findings-acl)
Copied to clipboard
| Challenge: | Relation extraction (RE) methods extract tuples of relationships from text . many datasets with frequent label errors have been used . |
| Approach: | They review recent surveys and a sample of recent RE methods papers . they find that real-time evaluations of RE methods are possible . |
| Outcome: | a sample of 38 datasets currently being used shows that many have frequent label errors . a small number of relations in specific domains can more realistically evaluate methods . |
AutoRE: Document-Level Relation Extraction with Large Language Models (2024.acl-demos)
Copied to clipboard
| Challenge: | Existing methods for relation extraction are limited to Sentence-level Relation Extraction (SentRE) tasks. |
| Approach: | They propose an end-to-end DocRE model that adopts a novel RE extraction paradigm named RHF (Relation-Head-Facts) Unlike existing approaches, AutoRE does not rely on the assumption of known relation options, making it more reflective of real-world scenarios. |
| Outcome: | The proposed model surpasses TAG by 10.03% and 9.03% on the dev and test set. |
Systematic Investigation of Strategies Tailored for Low-Resource Settings for Low-Resource Dependency Parsing (2023.eacl-main)
Copied to clipboard
| Challenge: | Several strategies have been proposed to enhance performance in low-resource scenarios. |
| Approach: | They propose to use 5 low-resource strategies for dependency parsing for multiple languages . they use ensembled approach on 7 UD low-rsource languages based on their results . |
| Outcome: | The proposed approach improves on a low-resource language Sanskrit. |
Revisiting Relation Extraction in the era of Large Language Models (2023.acl-long)
Copied to clipboard
| Challenge: | Standard supervised approaches to RE learn to tag tokens comprising entity spans and then predict the relationship between them. |
| Approach: | They propose to use large language models for RE to evaluate their performance . they use GPT-3 and Flan-T5 large to train RE . |
| Outcome: | The proposed model outperforms existing models on a sequence-to-sequence task under varying levels of supervision. |