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        <title>Graph-based Model Generation for Few-Shot Relation Extraction</title>
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            <title>Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing</title>
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            <namePart type="given">Yoav</namePart>
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            <namePart type="given">Yue</namePart>
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    <abstract>Few-shot relation extraction (FSRE) has been a challenging problem since it only has a handful of training instances. Existing models follow a ‘one-for-all’ scheme where one general large model performs all individual N-way-K-shot tasks in FSRE, which prevents the model from achieving the optimal point on each task. In view of this, we propose a model generation framework that consists of one general model for all tasks and many tiny task-specific models for each individual task. The general model generates and passes the universal knowledge to the tiny models which will be further fine-tuned when performing specific tasks. In this way, we decouple the complexity of the entire task space from that of all individual tasks while absorbing the universal knowledge. Extensive experimental results on two public datasets demonstrate that our framework reaches a new state-of-the-art performance for FRSE tasks. Our code is available at: https://github.com/NLPWM-WHU/GM_GEN.</abstract>
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    <identifier type="doi">10.18653/v1/2022.emnlp-main.5</identifier>
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%0 Conference Proceedings
%T Graph-based Model Generation for Few-Shot Relation Extraction
%A Li, Wanli
%A Qian, Tieyun
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F li-qian-2022-graph
%X Few-shot relation extraction (FSRE) has been a challenging problem since it only has a handful of training instances. Existing models follow a ‘one-for-all’ scheme where one general large model performs all individual N-way-K-shot tasks in FSRE, which prevents the model from achieving the optimal point on each task. In view of this, we propose a model generation framework that consists of one general model for all tasks and many tiny task-specific models for each individual task. The general model generates and passes the universal knowledge to the tiny models which will be further fine-tuned when performing specific tasks. In this way, we decouple the complexity of the entire task space from that of all individual tasks while absorbing the universal knowledge. Extensive experimental results on two public datasets demonstrate that our framework reaches a new state-of-the-art performance for FRSE tasks. Our code is available at: https://github.com/NLPWM-WHU/GM_GEN.
%R 10.18653/v1/2022.emnlp-main.5
%U https://aclanthology.org/2022.emnlp-main.5
%U https://doi.org/10.18653/v1/2022.emnlp-main.5
%P 62-71

Markdown (Informal)

[Graph-based Model Generation for Few-Shot Relation Extraction](https://aclanthology.org/2022.emnlp-main.5) (Li & Qian, EMNLP 2022)

• Graph-based Model Generation for Few-Shot Relation Extraction (Li & Qian, EMNLP 2022)

ACL

• Wanli Li and Tieyun Qian. 2022. Graph-based Model Generation for Few-Shot Relation Extraction. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 62–71, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.