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. |
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| Challenge: | Existing methods to predict relationships with given entity pairs are lacking in supervised methods. |
| Approach: | They propose a framework for zero-shot Relation Extraction that includes two modules: Custom Embedding and Dynamic Aggregation. |
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From Local Perspective to Global Reasoning: A Neuro-Symbolic Framework for Zero-Shot Relation Extraction (2026.findings-acl)
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| Challenge: | Existing methods for zero-shot relationship extraction do not distinguish between unseen, semantically similar relations. |
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Document-Level Zero-Shot Relation Extraction with Entity Side Information (2026.eacl-long)
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| Challenge: | Existing approaches rely on Large Language Models (LLMs) to generate synthetic data for unseen labels. |
| Approach: | They propose a document-level zero-shot relation extraction framework with Entity Side Information to solve existing problems. |
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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. |
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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. |
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Entity Concept-enhanced Few-shot Relation Extraction (2021.acl-short)
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| Challenge: | Existing FSRE methods fail to classify relations based on information of sentences and entity pairs due to limited samples and lack of knowledge. |
| Approach: | They propose a concept-sentence attention module to select the most appropriate concept from multiple concepts of each entity by calculating the semantic similarity between sentences and concepts. |
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Zshot: An Open-source Framework for Zero-Shot Named Entity Recognition and Relation Extraction (2023.acl-demo)
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Gabriele Picco, Marcos Martinez Galindo, Alberto Purpura, Leopold Fuchs, Vanessa Lopez, Thanh Lam Hoang
| Challenge: | ZSL is a machine learning field that uses textual descriptions of entities or relations to perform tasks that are not seen during training. |
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Grasping the Essentials: Tailoring Large Language Models for Zero-Shot Relation Extraction (2024.emnlp-main)
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| 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. |
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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 . |
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RE-Matching: A Fine-Grained Semantic Matching Method for Zero-Shot Relation Extraction (2023.acl-long)
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Jun Zhao, WenYu Zhan, Xin Zhao, Qi Zhang, Tao Gui, Zhongyu Wei, Junzhe Wang, Minlong Peng, Mingming Sun
| Challenge: | Existing methods for zero-shot relation extraction lack explicit modeling of matching pattern . et al. (2018) show that our method achieves higher matching accuracy and faster inference speed . |
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