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Found 13 papers, 3 papers with code
Keyphrase Extraction with Incomplete Annotated Training Data
Extracting keyphrases that summarize the main points of a document is a fundamental task in natural language processing.
An Unsupervised Multiple-Task and Multiple-Teacher Model for Cross-lingual Named Entity Recognition
In this study, based on the knowledge distillation framework and multi-task learning, we introduce the similarity metric model as an auxiliary task to improve the cross-lingual NER performance on the target domain.
E-VarM: Enhanced Variational Word Masks to Improve the Interpretability of Text Classification Models
Enhancing the interpretability of text classification models can help increase the reliability of these models in real-world applications.
GNNavigator: Towards Adaptive Training of Graph Neural Networks via Automatic Guideline Exploration
Graph Neural Networks (GNNs) succeed significantly in many applications recently.
Graph Neural Networks Automated Design and Deployment on Device-Edge Co-Inference Systems
GCoDE abstracts the device communication process into an explicit operation and fuses the search of architecture and the operations mapping in a unified space for joint-optimization.
DA-Net: A Disentangled and Adaptive Network for Multi-Source Cross-Lingual Transfer Learning
Secondly, we propose a class-aware parallel adaptation method that aligns class-level distributions for each source-target language pair, thereby alleviating the language pairs' language gap.
Poincaré Differential Privacy for Hierarchy-Aware Graph Embedding
Specifically, PoinDP first learns the hierarchy weights for each entity based on the Poincar\'e model in hyperbolic space.
Architectural Implications of GNN Aggregation Programming Abstractions
Graph neural networks (GNNs) have gained significant popularity due to the powerful capability to extract useful representations from graph data.
Adversarial Word Dilution as Text Data Augmentation in Low-Resource Regime
Data augmentation is widely used in text classification, especially in the low-resource regime where a few examples for each class are available during training.
Impact-Oriented Contextual Scholar Profiling using Self-Citation Graphs
Experiment outcomes show that the F1 score of best GF profile significantly outperforms alternative methods of impact indicators and bibliometric networks in all the 6 computer science fields considered.
Hardware-Aware Graph Neural Network Automated Design for Edge Computing Platforms
Moreover, HGNAS achieves hardware awareness during the GNN architecture design by leveraging a hardware performance predictor, which could balance the GNN model accuracy and efficiency corresponding to the characteristics of targeted devices.
ProKD: An Unsupervised Prototypical Knowledge Distillation Network for Zero-Resource Cross-Lingual Named Entity Recognition
Typically, these approaches adopt a teacher-student architecture, where the teacher network is trained in the source language, and the student network seeks to learn knowledge from the teacher network and is expected to perform well in the target language.
Optimizing Memory Efficiency of Graph Neural Networks on Edge Computing Platforms
Graph neural networks (GNN) have achieved state-of-the-art performance on various industrial tasks.
