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Relation-Guided Few-Shot Relational Triple Extraction

Authors:

Bin WangAuthors Info & Claims

SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 2206 - 2213

https://doi.org/10.1145/3477495.3531831

Published: 07 July 2022 Publication History

Abstract

In few-shot relational triple extraction (FS-RTE), one seeks to extract relational triples from plain texts by utilizing only few annotated samples. Recent work first extracts all entities and then classifies their relations. Such an entity-then-relation paradigm ignores the entity discrepancy between relations. To address it, we propose a novel task decomposition strategy, Relation-then-Entity, for FS-RTE. It first detects relations occurred in a sentence and then extracts the corresponding head/tail entities of the detected relations. To instantiate this strategy, we further propose a model, RelATE, which builds a dual-level attention to aggregate relation-relevant information to detect the relation occurrence and utilizes the annotated samples of the detected relations to extract the corresponding head/tail entities. Experimental results show that our model outperforms previous work by an absolute gain (18.98%, 28.85% in F1 in two few-shot settings).

Supplementary Material

MP4 File (SIGIR22-sp1672.mp4)

SIGIR22 Short Paper Presentation video of paper "Relation-Guided Few-Shot Relational Triple Extraction". In this paper, we propose a novel task decomposition strategy, Relation-then-Entity, for few-shot relational triple extraction to overcome the entity discrepancy problem between relations. We further propose a model, RelATE, to instantiate it. Experimental results show that our model outperforms previous work by an absolute gain.

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Cited By

Gao CZhang XJin ZShang WMa YLi LDing ZLiang Y(2024)Few-shot relational triple extraction with hierarchical prototype optimizationPattern Recognition10.1016/j.patcog.2024.110779156(110779)Online publication date: Dec-2024
https://doi.org/10.1016/j.patcog.2024.110779
Liao WLiu ZZhang YHuang XLiu NLiu TLi QLi XCai H(2024)Zero-shot relation triplet extraction as Next-Sentence PredictionKnowledge-Based Systems10.1016/j.knosys.2024.112507304(112507)Online publication date: Nov-2024
https://doi.org/10.1016/j.knosys.2024.112507
He XYan GHan XKan MRen Y(2024)Relation extraction method based on pre-trained model and bidirectional semantic unionInternational Journal of Machine Learning and Cybernetics10.1007/s13042-024-02334-wOnline publication date: 20-Sep-2024
https://doi.org/10.1007/s13042-024-02334-w
Show More Cited By

Index Terms

Relation-Guided Few-Shot Relational Triple Extraction
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Information extraction

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Information & Contributors

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Published In

cover image ACM Conferences

SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2022

3569 pages

ISBN:9781450387323

DOI:10.1145/3477495

General Chairs:
Enrique Amigo
UNED
,
Pablo Castells
UAM and Amazon
,
Julio Gonzalo
UNED
,
Program Chairs:
Ben Carterette
Spotify
,
J. Shane Culpepper
RMIT University
,
Gabriella Kazai
Waseda University

Copyright © 2022 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

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SIGIR: ACM Special Interest Group on Information Retrieval

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 July 2022

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Short-paper

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Youth Innovation Promotion Association of CAS
Strategic Priority Research Program of Chinese Academy of Sciences
National Key Research and Development Program of China

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SIGIR '22

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SIGIR

SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 11 - 15, 2022

Madrid, Spain

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Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

Gao CZhang XJin ZShang WMa YLi LDing ZLiang Y(2024)Few-shot relational triple extraction with hierarchical prototype optimizationPattern Recognition10.1016/j.patcog.2024.110779156(110779)Online publication date: Dec-2024
https://doi.org/10.1016/j.patcog.2024.110779
Liao WLiu ZZhang YHuang XLiu NLiu TLi QLi XCai H(2024)Zero-shot relation triplet extraction as Next-Sentence PredictionKnowledge-Based Systems10.1016/j.knosys.2024.112507304(112507)Online publication date: Nov-2024
https://doi.org/10.1016/j.knosys.2024.112507
He XYan GHan XKan MRen Y(2024)Relation extraction method based on pre-trained model and bidirectional semantic unionInternational Journal of Machine Learning and Cybernetics10.1007/s13042-024-02334-wOnline publication date: 20-Sep-2024
https://doi.org/10.1007/s13042-024-02334-w
Liu JQin XMa XRan W(2023)FREDA: Few-Shot Relation Extraction Based on Data AugmentationApplied Sciences10.3390/app1314831213:14(8312)Online publication date: 18-Jul-2023
https://doi.org/10.3390/app13148312
Yang CJiang SHe BMa CHe L(2023)Mutually Guided Few-Shot Learning For Relational Triple ExtractionICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP49357.2023.10096232(1-5)Online publication date: 4-Jun-2023
https://doi.org/10.1109/ICASSP49357.2023.10096232

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