A prompt-and-dynamic-filter PEFT design for multi-task dense prediction beats MTLoRA on PASCAL-Context with fewer trainable parameters.
Adversarial training for multi-context joint entity and relation extraction
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abstract
Adversarial training (AT) is a regularization method that can be used to improve the robustness of neural network methods by adding small perturbations in the training data. We show how to use AT for the tasks of entity recognition and relation extraction. In particular, we demonstrate that applying AT to a general purpose baseline model for jointly extracting entities and relations, allows improving the state-of-the-art effectiveness on several datasets in different contexts (i.e., news, biomedical, and real estate data) and for different languages (English and Dutch).
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TADFormer : Task-Adaptive Dynamic Transformer for Efficient Multi-Task Learning
A prompt-and-dynamic-filter PEFT design for multi-task dense prediction beats MTLoRA on PASCAL-Context with fewer trainable parameters.