mlsanityData to 7 Oct 2026

MapRE: An Effective Semantic Mapping Approach for Low-resource Relation Extraction

arXiv 2109.04108 ↗First posted 9 Sep 2021cs.CL cs.AIcited by 16

Not on the map. Why?

Abstract (arXiv)

Neural relation extraction models have shown promising results in recent years; however, the model performance drops dramatically given only a few training samples. Recent works try leveraging the advance in few-shot learning to solve the low resource problem, where they train label-agnostic models to directly compare the semantic similarities among context sentences in the embedding space. However, the label-aware information, i.e., the relation label that contains the semantic knowledge of the relation itself, is often neglected for prediction. In this work, we propose a framework considering both label-agnostic and label-aware semantic mapping information for low resource relation extraction. We show that incorporating the above two types of mapping information in both pretraining and fine-tuning can significantly improve the model performance on low-resource relation extraction tasks.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). Builds on 21 of its 36 references that are in the map. First posted on arXiv 9 Sep 2021.

Builds on

21 of 36 references.

References · 21

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 11,979
  3. 2018Representation Learning with Contrastive Predictive Codingcited by 7,662
  4. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,861
  5. 2016SQuAD: 100,000+ Questions for Machine Comprehension of Textcited by 4,115
  6. 2017Prototypical Networks for Few-shot Learningcited by 3,001
  7. 2018On First-Order Meta-Learning Algorithmscited by 1,003
  8. 2020Meta-Learning in Neural Networks: A Surveycited by 944
  9. 2019Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examplescited by 312
  10. 2019Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approachcited by 299
  11. 2019Matching the Blanks: Distributional Similarity for Relation Learningcited by 271
  12. 2017Zero-Shot Learning -- The Good, the Bad and the Uglycited by 254
9 more
  1. 2018FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluationcited by 254
  2. 2019FewRel 2.0: Towards More Challenging Few-Shot Relation Classificationcited by 94
  3. 2020Learning from Context or Names? An Empirical Study on Neural Relation Extractioncited by 75
  4. 2018Joint Embedding of Words and Labels for Text Classificationcited by 69
  5. 2019OpenNRE: An Open and Extensible Toolkit for Neural Relation Extractioncited by 62
  6. 2018Zero-shot User Intent Detection via Capsule Neural Networkscited by 54
  7. 2020Few-shot Relation Extraction via Bayesian Meta-learning on Relation Graphscited by 39
  8. 2019Multi-Level Matching and Aggregation Network for Few-Shot Relation Classificationcited by 32
  9. 2019Integrating Semantic Knowledge to Tackle Zero-shot Text Classificationcited by 28

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 8; 2023: 4; 2024: 3; 2025: 0; 2026: 1.

16 papers.

Sort:

  1. 2023Universal Information Extraction as Unified Semantic Matchingcited by 47
  2. 2023A Comprehensive Survey on Relation Extraction: Recent Advances and New Frontierscited by 21
  3. 2022Unified Semantic Typing with Meaningful Label Inferencecited by 18
  4. 2022Can NLI Provide Proper Indirect Supervision for Low-resource Biomedical Relation Extraction?cited by 13
  5. 2022Information Extraction in Low-Resource Scenarios: Survey and Perspectivecited by 10
  6. 2022A Simple yet Effective Relation Information Guided Approach for Few-Shot Relation Extractioncited by 9
  7. 2022Pre-training to Match for Unified Low-shot Relation Extractioncited by 8
  8. 2022Better Few-Shot Relation Extraction with Label Prompt Dropoutcited by 8
  9. 2023Synergistic Anchored Contrastive Pre-training for Few-Shot Relation Extractioncited by 5
  10. 2023PRiSM: Enhancing Low-Resource Document-Level Relation Extraction with Relation-Aware Score Calibrationcited by 3
  11. 2024Learning from Semi-Factuals: A Debiased and Semantic-Aware Framework for Generalized Relation Discoverycited by 2
  12. 2024Efficient Information Extraction in Few-Shot Relation Classification through Contrastive Representation Learningcited by 1
4 more
  1. 2022STAD: Self-Training with Ambiguous Data for Low-Resource Relation Extractioncited by 0
  2. 2022RAPS: A Novel Few-Shot Relation Extraction Pipeline with Query-Information Guided Attention and Adaptive Prototype Fusioncited by 0
  3. 2024Few-Shot Relation Extraction with Hybrid Visual Evidencecited by 0
  4. 2026Knowledge Graphs Meet Graph Neural Networks: A Comprehensive Surveycited by 0