168 citations · 178 across the 9 of their papers we have counts for
9 papers
Guiding AMR Parsing with Reverse Graph Linearization
Bofei Gao, Liang Chen, Peiyi Wang +2
Abstract Meaning Representation (AMR) parsing aims to extract an abstract semantic graph from a given sentence. The sequence-to-sequence approaches, which linearize the semantic gr…
SAILOR: Structural Augmentation Based Tail Node Representation Learning
Jie Liao, Jintang Li, Liang Chen +3
Graph Neural Networks (GNNs) have achieved state-of-the-art performance in representation learning for graphs recently. However, the effectiveness of GNNs, which capitalize on the…
Curriculum Modeling the Dependence among Targets with Multi-task Learning for Financial Marketing
Yunpeng Weng, Xing Tang, Liang Chen +1
Multi-task learning for various real-world applications usually involves tasks with logical sequential dependence. For example, in online marketing, the cascade behavior pattern of…
Improved Test-Time Adaptation for Domain Generalization
Liang Chen, Yong Zhang, Yibing Song +2
The main challenge in domain generalization (DG) is to handle the distribution shift problem that lies between the training and test data. Recent studies suggest that test-time tra…
HomoDistil: Homotopic Task-Agnostic Distillation of Pre-trained Transformers
Chen Liang, Haoming Jiang, Zheng Li +3
Knowledge distillation has been shown to be a powerful model compression approach to facilitate the deployment of pre-trained language models in practice. This paper focuses on tas…
Unified Functional Hashing in Automatic Machine Learning
Ryan Gillard, Stephen Jonany, Yingjie Miao +7
The field of Automatic Machine Learning (AutoML) has recently attained impressive results, including the discovery of state-of-the-art machine learning solutions, such as neural im…