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Found 6 papers, 2 papers with code
Parameter-efficient Continual Learning Framework in Industrial Real-time Text Classification System
Catastrophic forgetting is a challenge for model deployment in industrial real-time systems, which requires the model to quickly master a new task without forgetting the old one.
Towards Robust and Generalizable Training: An Empirical Study of Noisy Slot Filling for Input Perturbations
The proposed dataset contains five types of human-annotated noise, and all those noises are exactly existed in real extensive robust-training methods of slot filling into the proposed framework.
Generative Zero-Shot Prompt Learning for Cross-Domain Slot Filling with Inverse Prompting
Zero-shot cross-domain slot filling aims to transfer knowledge from the labeled source domain to the unlabeled target domain.
A Robust Contrastive Alignment Method For Multi-Domain Text Classification
In this paper, we propose a robust contrastive alignment method to align text classification features of various domains in the same feature space by supervised contrastive learning.
Bridge to Target Domain by Prototypical Contrastive Learning and Label Confusion: Re-explore Zero-Shot Learning for Slot Filling
Zero-shot cross-domain slot filling alleviates the data dependence in the case of data scarcity in the target domain, which has aroused extensive research.
Orthogonal Policy Gradient and Autonomous Driving Application
One less addressed issue of deep reinforcement learning is the lack of generalization capability based on new state and new target, for complex tasks, it is necessary to give the correct strategy and evaluate all possible actions for current state.
