Papers with Zero-Shot Relation Extraction
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. |
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. |
| Approach: | They propose a framework to enable global reasoning across a set of predictions. |
| Outcome: | The proposed framework outperforms existing methods and establishes new state-of-the-art results on widely used datasets. |