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Found 4 papers, 3 papers with code
Joint Entity and Relation Extraction with Span Pruning and Hypergraph Neural Networks
Also, most of current ERE models do not take into account higher-order interactions between multiple entities and relations, while higher-order modeling could be beneficial. In this work, we propose HyperGraph neural network for ERE ($\hgnn{}$), which is built upon the PL-marker (a state-of-the-art marker-based pipleline model).
Modeling Instance Interactions for Joint Information Extraction with Neural High-Order Conditional Random Field
Prior works on joint Information Extraction (IE) typically model instance (e. g., event triggers, entities, roles, relations) interactions by representation enhancement, type dependencies scoring, or global decoding.
Structural Knowledge Distillation: Tractably Distilling Information for Structured Predictor
The objective function of knowledge distillation is typically the cross-entropy between the teacher and the student's output distributions.
Controllable Length Control Neural Encoder-Decoder via Reinforcement Learning
Here, we denote a concept of Controllable Length Control (CLC) for the trade-off between length control capacity and semantic accuracy of the language generation model.
