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Found 5 papers, 3 papers with code
Incentivized Truthful Communication for Federated Bandits
To enhance the efficiency and practicality of federated bandit learning, recent advances have introduced incentives to motivate communication among clients, where a client participates only when the incentive offered by the server outweighs its participation cost.
Learning Semantic Textual Similarity via Topic-informed Discrete Latent Variables
Recently, discrete latent variable models have received a surge of interest in both Natural Language Processing (NLP) and Computer Vision (CV), attributed to their comparable performance to the continuous counterparts in representation learning, while being more interpretable in their predictions.
Handling Inter-class and Intra-class Imbalance in Class-imbalanced Learning
Class-imbalance is a common problem in machine learning practice.
A Novel Cascade Binary Tagging Framework for Relational Triple Extraction
Extracting relational triples from unstructured text is crucial for large-scale knowledge graph construction.
Ranked #5 on
Relation Extraction
on NYT11-HRL
Jointly Modeling Hierarchical and Horizontal Features for Relational Triple Extraction
In this work, we first introduce the hierarchical dependency and horizontal commonality between the two levels, and then propose an entity-enhanced dual tagging framework that enables the triple extraction (TE) task to utilize such interactions with self-learned entity features through an auxiliary entity extraction (EE) task, without breaking the joint decoding of relational triples.
