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Found 9 papers, 3 papers with code
FedED: Federated Learning via Ensemble Distillation for Medical Relation Extraction
In this paper, we propose a privacy-preserving medical relation extraction model based on federated learning, which enables training a central model with no single piece of private local data being shared or exchanged.
PathQG: Neural Question Generation from Facts
Human evaluation also proves that our model is able to generate relevant and informative questions.
Document-Level Relation Extraction via Pair-Aware and Entity-Enhanced Representation Learning
Document-level relation extraction aims to recognize relations among multiple entity pairs from a whole piece of article.
CFinBench: A Comprehensive Chinese Financial Benchmark for Large Language Models
(3) Financial Practice: whether LLMs can fulfill the practical financial jobs, such as tax consultant, junior accountant and securities analyst.
GAD-Generative Learning for HD Map-Free Autonomous Driving
Deep-learning-based techniques have been widely adopted for autonomous driving software stacks for mass production in recent years, focusing primarily on perception modules, with some work extending this method to prediction modules.
Enhancing Document-level Relation Extraction by Entity Knowledge Injection
Document-level relation extraction (RE) aims to identify the relations between entities throughout an entire document.
Ranked #23 on
Relation Extraction
on DocRED
Automatic Term Name Generation for Gene Ontology: Task and Dataset
Terms contained in Gene Ontology (GO) have been widely used in biology and bio-medicine.
Global-to-Local Neural Networks for Document-Level Relation Extraction
Relation extraction (RE) aims to identify the semantic relations between named entities in text.
Ranked #39 on
Relation Extraction
on DocRED
