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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…
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…
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…
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…
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…
ASAP: A Chinese Review Dataset Towards Aspect Category Sentiment Analysis and Rating Prediction
Jiahao Bu, Lei Ren, Shuang Zheng +4
Sentiment analysis has attracted increasing attention in e-commerce. The sentiment polarities underlying user reviews are of great value for business intelligence. Aspect category…