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    <abstract>Zero-shot Relation Extraction (ZSRE) aims to predict novel relations from sentences with given entity pairs, where the relations have not been encountered during training. Prototypebased methods, which achieve ZSRE by aligning the sentence representation and the relation prototype representation, have shown great potential. However, most existing works focus solely on improving the quality of prototype representations, neglecting sentence representations and lacking interaction between different types of relation side information. In this paper, we propose a novel ZSRE framework named CE-DA, which includes two modules: Custom Embedding and Dynamic Aggregation. We employ a two-stage approach to obtain customized embeddings of sentences. In the first stage, we train a sentence encoder through unsupervised contrastive learning, and in the second stage, we highlight the potential relations between entities in sentences using carefully designed entity emphasis prompts to further enhance sentence representations. Additionally, our dynamic aggregation method assigns different weights to different types of relation side information through a learnable network to enhance the quality of relation prototype representations. In contrast to traditional methods that treat the importance of all side information equally, our dynamic aggregation method further strengthen the interaction between different types of relation side information. Our method demonstrates competitive performance across various metrics on two ZSRE datasets.</abstract>
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%0 Conference Proceedings
%T CE-DA: Custom Embedding and Dynamic Aggregation for Zero-Shot Relation Extraction
%A Zhang, Fu
%A Liu, He
%A Li, Zehan
%A Cheng, Jingwei
%Y Rambow, Owen
%Y Wanner, Leo
%Y Apidianaki, Marianna
%Y Al-Khalifa, Hend
%Y Eugenio, Barbara Di
%Y Schockaert, Steven
%S Proceedings of the 31st International Conference on Computational Linguistics
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F zhang-etal-2025-ce
%X Zero-shot Relation Extraction (ZSRE) aims to predict novel relations from sentences with given entity pairs, where the relations have not been encountered during training. Prototypebased methods, which achieve ZSRE by aligning the sentence representation and the relation prototype representation, have shown great potential. However, most existing works focus solely on improving the quality of prototype representations, neglecting sentence representations and lacking interaction between different types of relation side information. In this paper, we propose a novel ZSRE framework named CE-DA, which includes two modules: Custom Embedding and Dynamic Aggregation. We employ a two-stage approach to obtain customized embeddings of sentences. In the first stage, we train a sentence encoder through unsupervised contrastive learning, and in the second stage, we highlight the potential relations between entities in sentences using carefully designed entity emphasis prompts to further enhance sentence representations. Additionally, our dynamic aggregation method assigns different weights to different types of relation side information through a learnable network to enhance the quality of relation prototype representations. In contrast to traditional methods that treat the importance of all side information equally, our dynamic aggregation method further strengthen the interaction between different types of relation side information. Our method demonstrates competitive performance across various metrics on two ZSRE datasets.
%U https://aclanthology.org/2025.coling-main.656/
%P 9814-9823

Markdown (Informal)

[CE-DA: Custom Embedding and Dynamic Aggregation for Zero-Shot Relation Extraction](https://aclanthology.org/2025.coling-main.656/) (Zhang et al., COLING 2025)

• CE-DA: Custom Embedding and Dynamic Aggregation for Zero-Shot Relation Extraction (Zhang et al., COLING 2025)

ACL

• Fu Zhang, He Liu, Zehan Li, and Jingwei Cheng. 2025. CE-DA: Custom Embedding and Dynamic Aggregation for Zero-Shot Relation Extraction. In Proceedings of the 31st International Conference on Computational Linguistics, pages 9814–9823, Abu Dhabi, UAE. Association for Computational Linguistics.