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    <abstract>Zero-shot relation extraction aims to identify relations between entity mentions using textual descriptions of novel types (i.e., previously unseen) instead of labeled training examples. Previous works often rely on unrealistic assumptions: (1) pairs of mentions are often encoded directly in the input, which prevents offline pre-computation for large scale document database querying; (2) no rejection mechanism is introduced, biasing the evaluation when using these models in a retrieval scenario where some (and often most) inputs are irrelevant and must be ignored. In this work, we study the robustness of existing zero-shot relation extraction models when adapting them to a realistic extraction scenario. To this end, we introduce a typology of existing models, and propose several strategies to build single pass models and models with a rejection mechanism. We adapt several state-of-the-art tools, and compare them in this challenging setting, showing that no existing work is really robust to realistic assumptions, but overall AlignRE (Li et al., 2024) performs best along all criteria.</abstract>
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%0 Conference Proceedings
%T A Study on Building Efficient Zero-Shot Relation Extraction Models
%A THOMAS, Hugo
%A Corro, Caio
%A Gravier, Guillaume
%A Sébillot, Pascale
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F thomas-etal-2026-study
%X Zero-shot relation extraction aims to identify relations between entity mentions using textual descriptions of novel types (i.e., previously unseen) instead of labeled training examples. Previous works often rely on unrealistic assumptions: (1) pairs of mentions are often encoded directly in the input, which prevents offline pre-computation for large scale document database querying; (2) no rejection mechanism is introduced, biasing the evaluation when using these models in a retrieval scenario where some (and often most) inputs are irrelevant and must be ignored. In this work, we study the robustness of existing zero-shot relation extraction models when adapting them to a realistic extraction scenario. To this end, we introduce a typology of existing models, and propose several strategies to build single pass models and models with a rejection mechanism. We adapt several state-of-the-art tools, and compare them in this challenging setting, showing that no existing work is really robust to realistic assumptions, but overall AlignRE (Li et al., 2024) performs best along all criteria.
%R 10.63317/5ndtgoxeph5j
%U https://aclanthology.org/2026.lrec-1.480/
%U https://doi.org/10.63317/5ndtgoxeph5j
%P 6056-6067

Markdown (Informal)

[A Study on Building Efficient Zero-Shot Relation Extraction Models](https://aclanthology.org/2026.lrec-1.480/) (THOMAS et al., LREC 2026)

• A Study on Building Efficient Zero-Shot Relation Extraction Models (THOMAS et al., LREC 2026)

ACL

• Hugo THOMAS, Caio Corro, Guillaume Gravier, and Pascale Sébillot. 2026. A Study on Building Efficient Zero-Shot Relation Extraction Models. In Proceedings of the Fifteenth Language Resources and Evaluation Conference, pages 6056–6067, Palma de Mallorca, Spain. ELRA Language Resource Association.