150 citations · 375 across the 20 of their papers we have counts for
7 papers · 1 filter
Meta-Learning with Dynamic-Memory-Based Prototypical Network for Few-Shot Event Detection
Shumin Deng, Ningyu Zhang, Jiaojian Kang +3
Event detection (ED), a sub-task of event extraction, involves identifying triggers and categorizing event mentions. Existing methods primarily rely upon supervised learning and re…
Relation Adversarial Network for Low Resource Knowledge Graph Completion
Ningyu Zhang, Shumin Deng, Zhanlin Sun +3
Knowledge Graph Completion (KGC) has been proposed to improve Knowledge Graphs by filling in missing connections via link prediction or relation extraction. One of the main difficu…
Transfer Learning for Relation Extraction via Relation-Gated Adversarial Learning
Ningyu Zhang, Shumin Deng, Zhanlin Sun +3
Relation extraction aims to extract relational facts from sentences. Previous models mainly rely on manually labeled datasets, seed instances or human-crafted patterns, and distant…
When Low Resource NLP Meets Unsupervised Language Model: Meta-pretraining Then Meta-learning for Few-shot Text Classification
Shumin Deng, Ningyu Zhang, Zhanlin Sun +2
Text classification tends to be difficult when data are deficient or when it is required to adapt to unseen classes. In such challenging scenarios, recent studies have often used m…
Augmenting Transfer Learning with Semantic Reasoning
Freddy Lecue, Jiaoyan Chen, Jeff Z. Pan +1
Transfer learning aims at building robust prediction models by transferring knowledge gained from one problem to another. In the semantic Web, learning tasks are enhanced with sema…
Interaction Embeddings for Prediction and Explanation in Knowledge Graphs
Wen Zhang, Bibek Paudel, Wei Zhang +2
Knowledge graph embedding aims to learn distributed representations for entities and relations, and is proven to be effective in many applications. Crossover interactions --- bi-di…
Human-centric Transfer Learning Explanation via Knowledge Graph [Extended Abstract]
Yuxia Geng, Jiaoyan Chen, Ernesto Jimenez-Ruiz +1
Transfer learning which aims at utilizing knowledge learned from one problem (source domain) to solve another different but related problem (target domain) has attracted wide resea…