mlsanityData to 7 Oct 2026

RAPS: A Novel Few-Shot Relation Extraction Pipeline with Query-Information Guided Attention and Adaptive Prototype Fusion

arXiv 2210.08242 ↗First posted 15 Oct 2022cs.CLcited by 0

Not on the map. Why?

Abstract (arXiv)

Few-shot relation extraction (FSRE) aims at recognizing unseen relations by learning with merely a handful of annotated instances. To generalize to new relations more effectively, this paper proposes a novel pipeline for the FSRE task based on queRy-information guided Attention and adaptive Prototype fuSion, namely RAPS. Specifically, RAPS first derives the relation prototype by the query-information guided attention module, which exploits rich interactive information between the support instances and the query instances, in order to obtain more accurate initial prototype representations. Then RAPS elaborately combines the derived initial prototype with the relation information by the adaptive prototype fusion mechanism to get the integrated prototype for both train and prediction. Experiments on the benchmark dataset FewRel 1.0 show a significant improvement of our method against state-of-the-art methods.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 22 of its 35 references that are in the map. First posted on arXiv 15 Oct 2022.

Builds on

22 of 35 references.

References · 22

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  3. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,861
  4. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,779
  5. 2017Prototypical Networks for Few-shot Learningcited by 3,001
  6. 2017Few-Shot Learning with Graph Neural Networkscited by 324
  7. 2019Matching the Blanks: Distributional Similarity for Relation Learningcited by 271
  8. 2018FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluationcited by 254
  9. 2019FewRel 2.0: Towards More Challenging Few-Shot Relation Classificationcited by 94
  10. 2020Learning from Context or Names? An Empirical Study on Neural Relation Extractioncited by 75
  11. 2019Cognitive Graph for Multi-Hop Reading Comprehension at Scalecited by 65
  12. 2019Learning Spatial Fusion for Single-Shot Object Detectioncited by 59
10 more
  1. 2020Few-shot Relation Extraction via Bayesian Meta-learning on Relation Graphscited by 39
  2. 2019Multi-Level Matching and Aggregation Network for Few-Shot Relation Classificationcited by 32
  3. 2021Exploring Task Difficulty for Few-Shot Relation Extractioncited by 24
  4. 2021Word Graph Guided Summarization for Radiology Findingscited by 19
  5. 2020Connecting Embeddings for Knowledge Graph Entity Typingcited by 17
  6. 2021MapRE: An Effective Semantic Mapping Approach for Low-resource Relation Extractioncited by 16
  7. 2021Entity Concept-enhanced Few-shot Relation Extractioncited by 14
  8. 2021Diverse Distributions of Self-Supervised Tasks for Meta-Learning in NLPcited by 10
  9. 2020Inductive Unsupervised Domain Adaptation for Few-Shot Classification via Clusteringcited by 8
  10. 2020Learning to Decouple Relations: Few-Shot Relation Classification with Entity-Guided Attention and Confusion-Aware Trainingcited by 7

Built on by

Not cited yet.