most citedDisenKGAT: Knowledge Graph Embedding with Disentangled Graph Attention Network

86 citations · 146 across the 4 of their papers we have counts for

collaborators

5 papers

cs.IR202113 cited

Popularity Bias Is Not Always Evil: Disentangling Benign and Harmful Bias for Recommendation

Zihao Zhao, Jiawei Chen, Sheng Zhou +4

Recommender system usually suffers from severe popularity bias -- the collected interaction data usually exhibits quite imbalanced or even long-tailed distribution over items. Such…

cs.CL2021

Virtual Data Augmentation: A Robust and General Framework for Fine-tuning Pre-trained Models

Kun Zhou, Wayne Xin Zhao, Sirui Wang +3

Recent works have shown that powerful pre-trained language models (PLM) can be fooled by small perturbations or intentional attacks. To solve this issue, various data augmentation…

cs.AI202186 cited

DisenKGAT: Knowledge Graph Embedding with Disentangled Graph Attention Network

Junkang Wu, Wentao Shi, Xuezhi Cao +5

Knowledge graph completion (KGC) has become a focus of attention across deep learning community owing to its excellent contribution to numerous downstream tasks. Although recently…

cs.CL202147 cited

ConSERT: A Contrastive Framework for Self-Supervised Sentence Representation Transfer

Yuanmeng Yan, Rumei Li, Sirui Wang +3

Learning high-quality sentence representations benefits a wide range of natural language processing tasks. Though BERT-based pre-trained language models achieve high performance on…

cs.IR2021

Improving Document Representations by Generating Pseudo Query Embeddings for Dense Retrieval

Hongyin Tang, Xingwu Sun, Beihong Jin +3

Recently, the retrieval models based on dense representations have been gradually applied in the first stage of the document retrieval tasks, showing better performance than tradit…