| 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. |
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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. |
Exploring the zero-shot limit of FewRel (2020.coling-main)
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| Challenge: | Existing methods to extract information from a language model are limited in their ability to generalize and do not perform as well as few-shot learning models. |
| Approach: | They propose a general purpose relation extractor that uses Wikidata descriptions to represent the relation’s surface form. |
| Outcome: | The proposed system is based on a FewRel 1.0 dataset, which provides an excellent framework for training and evaluating the proposed system in English. |
Frame First, Then Extract: A Frame-Semantic Reasoning Pipeline for Zero-Shot Relation Triplet Extraction (2025.emnlp-main)
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| Challenge: | Existing methods to extract triplets for unseen relations rely on costly fine-tuning and lack structured semantic guidance. |
| Approach: | They propose a framework that adopts a "frame first, then extract" paradigm to extract triplets from unstructured text. |
| Outcome: | The proposed framework achieves competitive zero-shot performance on multiple benchmarks and can be used to enhance existing extraction methods. |
Revisiting Large Language Models as Zero-shot Relation Extractors (2023.findings-emnlp)
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| Challenge: | Recent studies show that large language models (LLMs) transfer well to new tasks out-of-the-box . relationship extraction (RE) involves a certain degree of labeled or unlabeled data even under zero-shot setting. |
| Approach: | They propose a simple prompt recursively using LLMs to transform RE inputs to QA format . they propose qq prompting and qt prompting to improve their results . |
| Outcome: | The proposed method improves on different model sizes, benchmarks and settings. |
Pre-training to Match for Unified Low-shot Relation Extraction (2022.acl-long)
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| Challenge: | Low-shot relation extraction (RE) aims to recognize novel relations with very few or even no samples. |
| Approach: | They propose a method that leverages triplet paraphrase to pre-train zero-shot label matching ability and uses meta-learning paradigm to learn few-shot instance summarizing ability. |
| Outcome: | The proposed method outperforms strong baselines and achieves the best performance on few-shot RE leaderboard. |
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. |
Revisiting Relation Extraction in the era of Large Language Models (2023.acl-long)
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| 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. |
Re-Cent: A Relation-Centric Framework for Joint Zero-Shot Relation Triplet Extraction (2025.coling-main)
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| Challenge: | Existing methods to extract triplets from context often decompose into named entity recognition and relation classification, which may introduce error propagation. |
| Approach: | They propose a Relation-centric joint ZSRTE method which leverages unseen relation labels to extract triplets in one go. |
| Outcome: | The proposed method achieves state-of-the-art performance with fewer parameters and does not rely on synthetic data or manual labor. |
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. |
ZS-BERT: Towards Zero-Shot Relation Extraction with Attribute Representation Learning (2021.naacl-main)
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| Challenge: | Existing methods to relation extraction require labeled data, but labeling is difficult . Existing models cannot recognize rare instances that are never covered by training data . |
| Approach: | They propose a multi-task learning model that directly predicts unseen relations without hand-crafted attribute labeling and multiple pairwise classifications. |
| Outcome: | The proposed model outperforms existing methods by 13.54% on two well-known datasets. |