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

arXiv: https://arxiv.org/abs/1906.06678 · HTML: https://mlsanity.com/paper/1906.06678/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jun 2019.

## Built on by (most cited first, 30 of 32)

- [Prototypical Representation Learning for Relation Extraction](https://mlsanity.com/paper/2103.11647/) (2021)
- [CEFR-Based Sentence Difficulty Annotation and Assessment](https://mlsanity.com/paper/2210.11766/) (2022)
- [Meta Learning for Natural Language Processing: A Survey](https://mlsanity.com/paper/2205.01500/) (2022)
- [Exploring Task Difficulty for Few-Shot Relation Extraction](https://mlsanity.com/paper/2109.05473/) (2021)
- [MetaPrompting: Learning to Learn Better Prompts](https://mlsanity.com/paper/2209.11486/) (2022)
- [Read it Twice: Towards Faithfully Interpretable Fact Verification by Revisiting Evidence](https://mlsanity.com/paper/2305.03507/) (2023)
- [MapRE: An Effective Semantic Mapping Approach for Low-resource Relation Extraction](https://mlsanity.com/paper/2109.04108/) (2021)
- [Entity Concept-enhanced Few-shot Relation Extraction](https://mlsanity.com/paper/2106.02401/) (2021)
- [A Survey on Deep Learning Event Extraction: Approaches and Applications](https://mlsanity.com/paper/2107.02126/) (2021)
- [What Can Knowledge Bring to Machine Learning? -- A Survey of Low-shot Learning for Structured Data](https://mlsanity.com/paper/2106.06410/) (2021)
- [A Simple yet Effective Relation Information Guided Approach for Few-Shot Relation Extraction](https://mlsanity.com/paper/2205.09536/) (2022)
- [Pre-training to Match for Unified Low-shot Relation Extraction](https://mlsanity.com/paper/2203.12274/) (2022)
- [An Effective Automated Speaking Assessment Approach to Mitigating Data Scarcity and Imbalanced Distribution](https://mlsanity.com/paper/2404.07575/) (2024)
- [Learning to Bridge Metric Spaces: Few-shot Joint Learning of Intent Detection and Slot Filling](https://mlsanity.com/paper/2106.07343/) (2021)
- [Prompting with Phonemes: Enhancing LLMs' Multilinguality for Non-Latin Script Languages](https://mlsanity.com/paper/2411.02398/) (2024)
- [Function-words Enhanced Attention Networks for Few-Shot Inverse Relation Classification](https://mlsanity.com/paper/2204.12111/) (2022)
- [Synergistic Anchored Contrastive Pre-training for Few-Shot Relation Extraction](https://mlsanity.com/paper/2312.12021/) (2023)
- [Ensemble Making Few-Shot Learning Stronger](https://mlsanity.com/paper/2105.11904/) (2021)
- [Linguistic representations for fewer-shot relation extraction across domains](https://mlsanity.com/paper/2307.03823/) (2023)
- [HumanAL: Calibrating Human Matching Beyond a Single Task](https://mlsanity.com/paper/2205.03209/) (2022)
- [Few-shot Classification with Hypersphere Modeling of Prototypes](https://mlsanity.com/paper/2211.05319/) (2022)
- [Inconsistent Few-Shot Relation Classification via Cross-Attentional Prototype Networks with Contrastive Learning](https://mlsanity.com/paper/2110.08254/) (2021)
- [MGIMN: Multi-Grained Interactive Matching Network for Few-shot Text Classification](https://mlsanity.com/paper/2204.04952/) (2022)
- [Mutually Guided Few-shot Learning for Relational Triple Extraction](https://mlsanity.com/paper/2306.13310/) (2023)
- [Prompt Learning With Knowledge Memorizing Prototypes For Generalized Few-Shot Intent Detection](https://mlsanity.com/paper/2309.04971/) (2023)
- [Exploring the Limits of Natural Language Inference Based Setup for Few-Shot Intent Detection](https://mlsanity.com/paper/2112.07434/) (2021)
- [Classical Sequence Match is a Competitive Few-Shot One-Class Learner](https://mlsanity.com/paper/2209.06394/) (2022)
- [RAPS: A Novel Few-Shot Relation Extraction Pipeline with Query-Information Guided Attention and Adaptive Prototype Fusion](https://mlsanity.com/paper/2210.08242/) (2022)
- [A Few-Shot Learning Focused Survey on Recent Named Entity Recognition and Relation Classification Methods](https://mlsanity.com/paper/2310.19055/) (2023)
- [Few-Shot Relation Extraction with Hybrid Visual Evidence](https://mlsanity.com/paper/2403.00724/) (2024)

## 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.