@inproceedings{anh-etal-2025-mutual,
title = "Mutual-pairing Data Augmentation for Fewshot Continual Relation Extraction",
author = "Anh, Nguyen Hoang and
Tran, Quyen and
Nguyen, Thanh Xuan and
Diep, Nguyen Thi Ngoc and
Van, Linh Ngo and
Nguyen, Thien Huu and
Le, Trung",
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.205/",
doi = "10.18653/v1/2025.naacl-long.205",
pages = "4057--4075",
ISBN = "979-8-89176-189-6",
abstract = "Data scarcity is a major challenge in Few-shot Continual Relation Extraction (FCRE), where models must learn new relations from limited data while retaining past knowledge. Current methods, restricted by minimal data streams, struggle with catastrophic forgetting and overfitting. To overcome this, we introduce a novel *data augmentation strategy* that transforms single input sentences into complex texts by integrating both old and new data. Our approach sharpens model focus, enabling precise identification of word relationships based on specified relation types. By embedding adversarial training effects and leveraging new training perspectives through special objective functions, our method enhances model performance significantly. Additionally, we explore Sharpness-Aware Minimization (SAM) in Few-shot Continual Learning. Our extensive experiments uncover fascinating behaviors of SAM across tasks and offer valuable insights for future research in this dynamic field."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="anh-etal-2025-mutual">
<titleInfo>
<title>Mutual-pairing Data Augmentation for Fewshot Continual Relation Extraction</title>
</titleInfo>
<name type="personal">
<namePart type="given">Nguyen</namePart>
<namePart type="given">Hoang</namePart>
<namePart type="family">Anh</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Quyen</namePart>
<namePart type="family">Tran</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Thanh</namePart>
<namePart type="given">Xuan</namePart>
<namePart type="family">Nguyen</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Nguyen</namePart>
<namePart type="given">Thi</namePart>
<namePart type="given">Ngoc</namePart>
<namePart type="family">Diep</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Linh</namePart>
<namePart type="given">Ngo</namePart>
<namePart type="family">Van</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Thien</namePart>
<namePart type="given">Huu</namePart>
<namePart type="family">Nguyen</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Trung</namePart>
<namePart type="family">Le</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2025-04</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>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)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Luis</namePart>
<namePart type="family">Chiruzzo</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Alan</namePart>
<namePart type="family">Ritter</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Lu</namePart>
<namePart type="family">Wang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Albuquerque, New Mexico</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-189-6</identifier>
</relatedItem>
<abstract>Data scarcity is a major challenge in Few-shot Continual Relation Extraction (FCRE), where models must learn new relations from limited data while retaining past knowledge. Current methods, restricted by minimal data streams, struggle with catastrophic forgetting and overfitting. To overcome this, we introduce a novel *data augmentation strategy* that transforms single input sentences into complex texts by integrating both old and new data. Our approach sharpens model focus, enabling precise identification of word relationships based on specified relation types. By embedding adversarial training effects and leveraging new training perspectives through special objective functions, our method enhances model performance significantly. Additionally, we explore Sharpness-Aware Minimization (SAM) in Few-shot Continual Learning. Our extensive experiments uncover fascinating behaviors of SAM across tasks and offer valuable insights for future research in this dynamic field.</abstract>
<identifier type="citekey">anh-etal-2025-mutual</identifier>
<identifier type="doi">10.18653/v1/2025.naacl-long.205</identifier>
<location>
<url>https://aclanthology.org/2025.naacl-long.205/</url>
</location>
<part>
<date>2025-04</date>
<extent unit="page">
<start>4057</start>
<end>4075</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Mutual-pairing Data Augmentation for Fewshot Continual Relation Extraction
%A Anh, Nguyen Hoang
%A Tran, Quyen
%A Nguyen, Thanh Xuan
%A Diep, Nguyen Thi Ngoc
%A Van, Linh Ngo
%A Nguyen, Thien Huu
%A Le, Trung
%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 anh-etal-2025-mutual
%X Data scarcity is a major challenge in Few-shot Continual Relation Extraction (FCRE), where models must learn new relations from limited data while retaining past knowledge. Current methods, restricted by minimal data streams, struggle with catastrophic forgetting and overfitting. To overcome this, we introduce a novel *data augmentation strategy* that transforms single input sentences into complex texts by integrating both old and new data. Our approach sharpens model focus, enabling precise identification of word relationships based on specified relation types. By embedding adversarial training effects and leveraging new training perspectives through special objective functions, our method enhances model performance significantly. Additionally, we explore Sharpness-Aware Minimization (SAM) in Few-shot Continual Learning. Our extensive experiments uncover fascinating behaviors of SAM across tasks and offer valuable insights for future research in this dynamic field.
%R 10.18653/v1/2025.naacl-long.205
%U https://aclanthology.org/2025.naacl-long.205/
%U https://doi.org/10.18653/v1/2025.naacl-long.205
%P 4057-4075
Markdown (Informal)
[Mutual-pairing Data Augmentation for Fewshot Continual Relation Extraction](https://aclanthology.org/2025.naacl-long.205/) (Anh et al., NAACL 2025)
ACL
- Nguyen Hoang Anh, Quyen Tran, Thanh Xuan Nguyen, Nguyen Thi Ngoc Diep, Linh Ngo Van, Thien Huu Nguyen, and Trung Le. 2025. Mutual-pairing Data Augmentation for Fewshot Continual Relation Extraction. In 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), pages 4057–4075, Albuquerque, New Mexico. Association for Computational Linguistics.