REPaperList

😆 Must-read papers on relation extraction especifically for low-resource setting.

Datasets

Dataset	#train	#dev	#test	#rel	no_relation	entity type
TACRED [link | paper]	68,124	22,631	15,509	42	✔	✔
TACREV [link | paper]	68,124	22,631	15,509	42	✔	✔
Re-TACRED [link | paper]	58,465	19,584	13,418	40	✔	✔
Wiki80 [link | paper]	50,400	5,600	--	80	✘	✘
FewRel 1.0 [link | paper]	44,800	11,200	14,000*	100 (64/16/20)	✘	✘
FewRel 2.0 [link | paper]	44,800	2,500		(64 / 25)	✘	✘

(*--> unpublic)

Dataset	#train	#dev	#test	#rel	#tuples (train | test)	entity overlap type (NEO/EPO/SEO)
NYT24 [link | paper]	56,196		5,000	24	88,366 | 8,120	37,371 / 15,124 / 18,825 | 3,289 / 1,410 / 1,711
NYT29 [link | paper]	63,306		4,006	29	78,973 | 5,859	53,444 / 8,379 / 9,862 | 2,963 / 898 / 1,043
WebNLG [link | paper ]	5,019	500	703	216
ACE05 [link]
ACE04 [link]
SciERC [link | paper ]	1,861	275	551	7

Papers

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Low-resource relation extraction

N-way-K-shot setups

• FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation. [pdf], [project]

• FewRel 2.0: Towards More Challenging Few-Shot Relation Classification. [pdf], [project]

• Hybrid Attention-Based Prototypical Networks for Noisy Few-Shot Relation Classification. [pdf], [project]

• Multi-Level Matching and Aggregation Network for Few-Shot Relation Classification. [pdf], [project]

• Matching the Blanks: Distributional Similarity for Relation Learning. [pdf], [project]

• Hierarchical Attention Prototypical Networks for Few-Shot Text Classification. [pdf ]

• Few-shot Relation Extraction via Bayesian Meta-learning on Relation Graphs. [pdf], [project]

• Enhance Prototypical Network with Text Descriptions for Few-shot Relation Classification. [pdf]

• Learning from Context or Names? An Empirical Study on Neural Relation Extraction. [pdf], [project]

• Bridging Text and Knowledge with Multi-Prototype Embedding for Few-Shot Relational Triple Extraction. [pdf]

• Entity Concept-enhanced Few-shot Relation Extraction. [pdf], [project]

• Learning Discriminative and Unbiased Representations for Few-Shot Relation Extraction. [pdf]]

• Zero-shot Relation Classification from Side Information. [pdf], [project]

• MapRE: An Effective Semantic Mapping Approach for Low-resource Relation Extraction [pdf]

• Exploring Task Difficulty for Few-Shot Relation Extraction. [pdf], [project]

• Towards Realistic Few-Shot Relation Extraction. [pdf], [project]

Generalized few-shot setups

• KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extraction. [pdf], [project]
• PTR: Prompt Tuning with Rules for Text Classification. [pdf], [project]
• GradLRE: Gradient Imitation Reinforcement Learning for Low resource Relation Extraction. [pdf], [project]
• Label Verbalization and Entailment for Effective Zero and Few-Shot Relation Extraction. [pdf], [project]

Triple Extraction

Joint Extraction of Entities and Relations

Sentence-level relation extraction

Transformer-based models