10 citations · 18 across the 3 of their papers we have counts for
6 papers
Syntax-BERT: Improving Pre-trained Transformers with Syntax Trees
Jiangang Bai, Yujing Wang, Yiren Chen +4
Pre-trained language models like BERT achieve superior performances in various NLP tasks without explicit consideration of syntactic information. Meanwhile, syntactic information h…
Evolving Attention with Residual Convolutions
Yujing Wang, Yaming Yang, Jiangang Bai +6
Transformer is a ubiquitous model for natural language processing and has attracted wide attentions in computer vision. The attention maps are indispensable for a transformer model…
AutoADR: Automatic Model Design for Ad Relevance
Yiren Chen, Yaming Yang, Hong Sun +7
Large-scale pre-trained models have attracted extensive attention in the research community and shown promising results on various tasks of natural language processing. However, th…
LadaBERT: Lightweight Adaptation of BERT through Hybrid Model Compression
Yihuan Mao, Yujing Wang, Chufan Wu +6
BERT is a cutting-edge language representation model pre-trained by a large corpus, which achieves superior performances on various natural language understanding tasks. However, a…
TextNAS: A Neural Architecture Search Space tailored for Text Representation
Yujing Wang, Yaming Yang, Yiren Chen +7
Learning text representation is crucial for text classification and other language related tasks. There are a diverse set of text representation networks in the literature, and how…
DeGNN: Characterizing and Improving Graph Neural Networks with Graph Decomposition
Xupeng Miao, Nezihe Merve Gürel, Wentao Zhang +17
Despite the wide application of Graph Convolutional Network (GCN), one major limitation is that it does not benefit from the increasing depth and suffers from the oversmoothing pro…