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Found 10 papers, 6 papers with code
Improved Dense Nested Attention Network Based on Transformer for Infrared Small Target Detection
To address this issue, we propose a novel method for detecting infrared small targets called improved dense nested attention network (IDNANet), which is based on the transformer architecture.
Revisit Input Perturbation Problems for LLMs: A Unified Robustness Evaluation Framework for Noisy Slot Filling Task
To address these challenges, we propose a unified robustness evaluation framework based on the slot-filling task to systematically evaluate the dialogue understanding capability of LLMs in diverse input perturbation scenarios.
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.
A Multi-Task Semantic Decomposition Framework with Task-specific Pre-training for Few-Shot NER
The objective of few-shot named entity recognition is to identify named entities with limited labeled instances.
Ranked #1 on
Few-shot NER
on Few-NERD (INTER)
A Prototypical Semantic Decoupling Method via Joint Contrastive Learning for Few-Shot Name Entity Recognition
Specifically, we decouple class-specific prototypes and contextual semantic prototypes by two masking strategies to lead the model to focus on two different semantic information for inference.
Revisit Out-Of-Vocabulary Problem for Slot Filling: A Unified Contrastive Frameword with Multi-level Data Augmentations
In real dialogue scenarios, the existing slot filling model, which tends to memorize entity patterns, has a significantly reduced generalization facing Out-of-Vocabulary (OOV) problems.
Semi-Supervised Knowledge-Grounded Pre-training for Task-Oriented Dialog Systems
Recent advances in neural approaches greatly improve task-oriented dialogue (TOD) systems which assist users to accomplish their goals.
PSSAT: A Perturbed Semantic Structure Awareness Transferring Method for Perturbation-Robust Slot Filling
Most existing slot filling models tend to memorize inherent patterns of entities and corresponding contexts from training data.
A fully differentiable ligand pose optimization framework guided by deep learning and traditional scoring functions
In this work, we propose a fully differentiable framework for ligand pose optimization based on a hybrid scoring function (SF) combined with a multi-layer perceptron (DeepRMSD) and the traditional AutoDock Vina SF.
OnionNet-2: A Convolutional Neural Network Model for Predicting Protein-Ligand Binding Affinity based on Residue-Atom Contacting Shells
In this study, we proposed a simple scoring function (called OnionNet-2) based on convolutional neural network to predict $\triangle$$G$.
