5 papers
Lightweight, Dynamic Graph Convolutional Networks for AMR-to-Text Generation
Yan Zhang, Zhijiang Guo, Zhiyang Teng +4
AMR-to-text generation is used to transduce Abstract Meaning Representation structures (AMR) into text. A key challenge in this task is to efficiently learn effective graph represe…
An Unsupervised Sentence Embedding Method by Mutual Information Maximization
Yan Zhang, Ruidan He, Zuozhu Liu +2
BERT is inefficient for sentence-pair tasks such as clustering or semantic search as it needs to evaluate combinatorially many sentence pairs which is very time-consuming. Sentence…
Better Set Representations For Relational Reasoning
Qian Huang, Horace He, Abhay Singh +3
Incorporating relational reasoning into neural networks has greatly expanded their capabilities and scope. One defining trait of relational reasoning is that it operates on a set o…
Densely Connected Graph Convolutional Networks for Graph-to-Sequence Learning
Zhijiang Guo, Yan Zhang, Zhiyang Teng +1
We focus on graph-to-sequence learning, which can be framed as transducing graph structures to sequences for text generation. To capture structural information associated with grap…
Attention Guided Graph Convolutional Networks for Relation Extraction
Zhijiang Guo, Yan Zhang, Wei Lu
Dependency trees convey rich structural information that is proven useful for extracting relations among entities in text. However, how to effectively make use of relevant informat…