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Multi-Level Matching and Aggregation Network for Few-Shot Relation Classification

arXiv 1906.06678 ↗First posted 16 Jun 2019cs.CLcited by 32

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Abstract (arXiv)

This paper presents a multi-level matching and aggregation network (MLMAN) for few-shot relation classification. Previous studies on this topic adopt prototypical networks, which calculate the embedding vector of a query instance and the prototype vector of each support set independently. In contrast, our proposed MLMAN model encodes the query instance and each support set in an interactive way by considering their matching information at both local and instance levels. The final class prototype for each support set is obtained by attentive aggregation over the representations of its support instances, where the weights are calculated using the query instance. Experimental results demonstrate the effectiveness of our proposed methods, which achieve a new state-of-the-art performance on the FewRel dataset.

Key facts. Cited by 32 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Jun 2019.

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Citing papers per year: 2021: 10; 2022: 11; 2023: 6; 2024: 5.

32 papers.

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  1. 2021Prototypical Representation Learning for Relation Extractioncited by 33
  2. 2022CEFR-Based Sentence Difficulty Annotation and Assessmentcited by 28
  3. 2022Meta Learning for Natural Language Processing: A Surveycited by 26
  4. 2021Exploring Task Difficulty for Few-Shot Relation Extractioncited by 24
  5. 2022MetaPrompting: Learning to Learn Better Promptscited by 19
  6. 2023Read it Twice: Towards Faithfully Interpretable Fact Verification by Revisiting Evidencecited by 17
  7. 2021MapRE: An Effective Semantic Mapping Approach for Low-resource Relation Extractioncited by 16
  8. 2021Entity Concept-enhanced Few-shot Relation Extractioncited by 14
  9. 2021A Survey on Deep Learning Event Extraction: Approaches and Applicationscited by 12
  10. 2021What Can Knowledge Bring to Machine Learning? -- A Survey of Low-shot Learning for Structured Datacited by 10
  11. 2022A Simple yet Effective Relation Information Guided Approach for Few-Shot Relation Extractioncited by 9
  12. 2022Pre-training to Match for Unified Low-shot Relation Extractioncited by 8
20 more
  1. 2024An Effective Automated Speaking Assessment Approach to Mitigating Data Scarcity and Imbalanced Distributioncited by 7
  2. 2021Learning to Bridge Metric Spaces: Few-shot Joint Learning of Intent Detection and Slot Fillingcited by 6
  3. 2024Prompting with Phonemes: Enhancing LLMs' Multilinguality for Non-Latin Script Languagescited by 6
  4. 2022Function-words Enhanced Attention Networks for Few-Shot Inverse Relation Classificationcited by 5
  5. 2023Synergistic Anchored Contrastive Pre-training for Few-Shot Relation Extractioncited by 5
  6. 2021Ensemble Making Few-Shot Learning Strongercited by 4
  7. 2023Linguistic representations for fewer-shot relation extraction across domainscited by 4
  8. 2022HumanAL: Calibrating Human Matching Beyond a Single Taskcited by 2
  9. 2022Few-shot Classification with Hypersphere Modeling of Prototypescited by 2
  10. 2021Inconsistent Few-Shot Relation Classification via Cross-Attentional Prototype Networks with Contrastive Learningcited by 1
  11. 2022MGIMN: Multi-Grained Interactive Matching Network for Few-shot Text Classificationcited by 1
  12. 2023Mutually Guided Few-shot Learning for Relational Triple Extractioncited by 1
  13. 2023Prompt Learning With Knowledge Memorizing Prototypes For Generalized Few-Shot Intent Detectioncited by 1
  14. 2021Exploring the Limits of Natural Language Inference Based Setup for Few-Shot Intent Detectioncited by 0
  15. 2022Classical Sequence Match is a Competitive Few-Shot One-Class Learnercited by 0
  16. 2022RAPS: A Novel Few-Shot Relation Extraction Pipeline with Query-Information Guided Attention and Adaptive Prototype Fusioncited by 0
  17. 2023A Few-Shot Learning Focused Survey on Recent Named Entity Recognition and Relation Classification Methodscited by 0
  18. 2024Few-Shot Relation Extraction with Hybrid Visual Evidencecited by 0
  19. 2024Perturbing the Gradient for Alleviating Meta Overfittingcited by 0
  20. 2024Few-shot Open Relation Extraction with Gaussian Prototype and Adaptive Margincited by 0