Few-Shot, No Problem: Descriptive Continual Relation Extraction
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866910849967325184 |
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| author | Thanh, Nguyen Xuan Le, Anh Duc Tran, Quyen Le, Thanh-Thien Van, Linh Ngo Nguyen, Thien Huu |
| author_facet | Thanh, Nguyen Xuan Le, Anh Duc Tran, Quyen Le, Thanh-Thien Van, Linh Ngo Nguyen, Thien Huu |
| contents | Few-shot Continual Relation Extraction is a crucial challenge for enabling AI systems to identify and adapt to evolving relationships in dynamic real-world domains. Traditional memory-based approaches often overfit to limited samples, failing to reinforce old knowledge, with the scarcity of data in few-shot scenarios further exacerbating these issues by hindering effective data augmentation in the latent space. In this paper, we propose a novel retrieval-based solution, starting with a large language model to generate descriptions for each relation. From these descriptions, we introduce a bi-encoder retrieval training paradigm to enrich both sample and class representation learning. Leveraging these enhanced representations, we design a retrieval-based prediction method where each sample "retrieves" the best fitting relation via a reciprocal rank fusion score that integrates both relation description vectors and class prototypes. Extensive experiments on multiple datasets demonstrate that our method significantly advances the state-of-the-art by maintaining robust performance across sequential tasks, effectively addressing catastrophic forgetting. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_20596 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Few-Shot, No Problem: Descriptive Continual Relation Extraction Thanh, Nguyen Xuan Le, Anh Duc Tran, Quyen Le, Thanh-Thien Van, Linh Ngo Nguyen, Thien Huu Computation and Language Few-shot Continual Relation Extraction is a crucial challenge for enabling AI systems to identify and adapt to evolving relationships in dynamic real-world domains. Traditional memory-based approaches often overfit to limited samples, failing to reinforce old knowledge, with the scarcity of data in few-shot scenarios further exacerbating these issues by hindering effective data augmentation in the latent space. In this paper, we propose a novel retrieval-based solution, starting with a large language model to generate descriptions for each relation. From these descriptions, we introduce a bi-encoder retrieval training paradigm to enrich both sample and class representation learning. Leveraging these enhanced representations, we design a retrieval-based prediction method where each sample "retrieves" the best fitting relation via a reciprocal rank fusion score that integrates both relation description vectors and class prototypes. Extensive experiments on multiple datasets demonstrate that our method significantly advances the state-of-the-art by maintaining robust performance across sequential tasks, effectively addressing catastrophic forgetting. |
| title | Few-Shot, No Problem: Descriptive Continual Relation Extraction |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2502.20596 |