TACRED

Overview edit

Variants: TACRED

Associated Benchmarks

This dataset is used in 2 benchmarks:

• Relation Extraction - Metrics: F1, F1 (10% Few-Shot), F1 (5% Few-Shot), F1 (1% Few-Shot), F1 (Zero-Shot)
• Relation Classification - Metrics: F1

Recent Benchmark Submissions

Research Papers

Recent papers with results on this dataset:

• Retrieval-Augmented Generation-based Relation Extraction (2024) -
• Aligning Instruction Tasks Unlocks Large Language Models as Zero-Shot Relation Extractors (2023) -
• Sequence Generation with Label Augmentation for Relation Extraction (2022) -
• Generative Prompt Tuning for Relation Classification (2022) -
• DeepStruct: Pretraining of Language Models for Structure Prediction (2022) -
• Summarization as Indirect Supervision for Relation Extraction (2022) -
• Unified Semantic Typing with Meaningful Label Inference (2022) -
• Zero-Shot Information Extraction as a Unified Text-to-Triple Translation (2021) -
• Label Verbalization and Entailment for Effective Zero- and Few-Shot Relation Extraction (2021) -
• Improving Sentence-Level Relation Extraction through Curriculum Learning (2021) -
• Relation Classification with Entity Type Restriction (2021) -
• Learning from Noisy Labels for Entity-Centric Information Extraction (2021) -
• An Improved Baseline for Sentence-level Relation Extraction (2021) -
• Structured Prediction as Translation between Augmented Natural Languages (2021) -
• GDPNet: Refining Latent Multi-View Graph for Relation Extraction (2020) -
• Relation Classification as Two-way Span-Prediction (2020) -
• Learning from Context or Names? An Empirical Study on Neural Relation Extraction (2020) -
• LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention (2020) -
• Efficient long-distance relation extraction with DG-SpanBERT (2020) -
• K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters (2020) -