Search Results for author:
Found 8 papers, 2 papers with code
TC-GAT: Graph Attention Network for Temporal Causality Discovery
The present study explores the intricacies of causal relationship extraction, a vital component in the pursuit of causality knowledge.
Label-aware Multi-level Contrastive Learning for Cross-lingual Spoken Language Understanding
Despite the great success of spoken language understanding (SLU) in high-resource languages, it remains challenging in low-resource languages mainly due to the lack of labeled training data.
Reinforced Iterative Knowledge Distillation for Cross-Lingual Named Entity Recognition
Named entity recognition (NER) is a fundamental component in many applications, such as Web Search and Voice Assistants.
A Review of Dataset and Labeling Methods for Causality Extraction
Moreover, there is a lack of unified causal sequence label methods, which constitute the key factors that hinder the progress of causality extraction research.
CalibreNet: Calibration Networks for Multilingual Sequence Labeling
To tackle the challenge of lack of training data in low-resource languages, we dedicatedly develop a novel unsupervised phrase boundary recovery pre-training task to enhance the multilingual boundary detection capability of CalibreNet.
A Block-based Generative Model for Attributed Networks Embedding
To preserve the attribute information, we assume that each node has a hidden embedding related to its assigned block.
A Multi-level Neural Network for Implicit Causality Detection in Web Texts
Mining causality from text is a complex and crucial natural language understanding task corresponding to the human cognition.
Low Cost Edge Sensing for High Quality Demosaicking
Compared to methods of similar computational cost, our method achieves substantially higher accuracy, Whereas compared to methods of similar accuracy, our method has significantly lower cost.
