@inproceedings{le-etal-2025-enhancing,
title = "Enhancing Discriminative Representation in Similar Relation Clusters for Few-Shot Continual Relation Extraction",
author = "Le, Anh Duc and
Hai, Nam Le and
Nguyen, Thanh Xuan and
Van, Linh Ngo and
Diep, Nguyen Thi Ngoc and
Dinh, Sang and
Nguyen, Thien Huu",
editor = "Chiruzzo, Luis and
Ritter, Alan and
Wang, Lu",
booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.naacl-long.123/",
doi = "10.18653/v1/2025.naacl-long.123",
pages = "2450--2467",
ISBN = "979-8-89176-189-6",
abstract = "Few-shot Continual Relation Extraction (FCRE) has emerged as a significant challenge in information extraction, necessitating that relation extraction (RE) systems can sequentially identify new relations with limited labeled samples. While existing studies have demonstrated promising results in FCRE, they often overlook the issue of similar relations, which is a critical factor contributing to catastrophic forgetting. In this work, we propose Sirus{--}a novel method that utilizes relation descriptions and dynamic clustering on these descriptions to identify similar relations. Leveraging this information, we introduce innovative loss functions specifically designed to enhance the distinction between relations, with a focus on learning to differentiate similar ones. Experimental results show that our approach can effectively mitigate the problem of catastrophic forgetting and outperforms state-of-the-art methods by a large margin. Additionally, we explore the potential of Large Language Model Embeddings (LLMEs) with representation learning and embedding capabilities, demonstrating their promise for advancing FCRE systems."
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<abstract>Few-shot Continual Relation Extraction (FCRE) has emerged as a significant challenge in information extraction, necessitating that relation extraction (RE) systems can sequentially identify new relations with limited labeled samples. While existing studies have demonstrated promising results in FCRE, they often overlook the issue of similar relations, which is a critical factor contributing to catastrophic forgetting. In this work, we propose Sirus–a novel method that utilizes relation descriptions and dynamic clustering on these descriptions to identify similar relations. Leveraging this information, we introduce innovative loss functions specifically designed to enhance the distinction between relations, with a focus on learning to differentiate similar ones. Experimental results show that our approach can effectively mitigate the problem of catastrophic forgetting and outperforms state-of-the-art methods by a large margin. Additionally, we explore the potential of Large Language Model Embeddings (LLMEs) with representation learning and embedding capabilities, demonstrating their promise for advancing FCRE systems.</abstract>
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%0 Conference Proceedings
%T Enhancing Discriminative Representation in Similar Relation Clusters for Few-Shot Continual Relation Extraction
%A Le, Anh Duc
%A Hai, Nam Le
%A Nguyen, Thanh Xuan
%A Van, Linh Ngo
%A Diep, Nguyen Thi Ngoc
%A Dinh, Sang
%A Nguyen, Thien Huu
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-189-6
%F le-etal-2025-enhancing
%X Few-shot Continual Relation Extraction (FCRE) has emerged as a significant challenge in information extraction, necessitating that relation extraction (RE) systems can sequentially identify new relations with limited labeled samples. While existing studies have demonstrated promising results in FCRE, they often overlook the issue of similar relations, which is a critical factor contributing to catastrophic forgetting. In this work, we propose Sirus–a novel method that utilizes relation descriptions and dynamic clustering on these descriptions to identify similar relations. Leveraging this information, we introduce innovative loss functions specifically designed to enhance the distinction between relations, with a focus on learning to differentiate similar ones. Experimental results show that our approach can effectively mitigate the problem of catastrophic forgetting and outperforms state-of-the-art methods by a large margin. Additionally, we explore the potential of Large Language Model Embeddings (LLMEs) with representation learning and embedding capabilities, demonstrating their promise for advancing FCRE systems.
%R 10.18653/v1/2025.naacl-long.123
%U https://aclanthology.org/2025.naacl-long.123/
%U https://doi.org/10.18653/v1/2025.naacl-long.123
%P 2450-2467
Markdown (Informal)
[Enhancing Discriminative Representation in Similar Relation Clusters for Few-Shot Continual Relation Extraction](https://aclanthology.org/2025.naacl-long.123/) (Le et al., NAACL 2025)
ACL