Fangzhao Wu
19 papers in the PaperMetrix corpus
Papers by this author
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Neural Chinese Word Segmentation with Dictionary Knowledge
2018 · arXiv (Cornell University)
Chinese word segmentation (CWS) is an important task for Chinese NLP. Recently, many neural network based methods have been proposed for CWS. However, these methods require a large number of labeled sentences for model training, …
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Imbalanced Sentiment Classification with Multi-Task Learning
2018
Supervised learning methods are widely used in sentiment classification. However, when sentiment distribution is imbalanced, the performance of these methods declines. In this paper, we propose an effective approach for imbalanced sentiment classification. In our …
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DRr-Net: Dynamic Re-Read Network for Sentence Semantic Matching
2019 · Proceedings of the AAAI Conference on Artificial Intelligence
Sentence semantic matching requires an agent to determine the semantic relation between two sentences, which is widely used in various natural language tasks such as Natural Language Inference (NLI) and Paraphrase Identification (PI). Among all …
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Neural Review Rating Prediction with User and Product Memory
2019
Neural network methods have achieved great success in sentiment classification. Recent studies have found that incorporating user and product information can effectively improve the performance of review sentiment classification. However, most of these studies only …
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Neural News Recommendation with Negative Feedback
2021 · arXiv (Cornell University)
News recommendation is important for online news services. Precise user interest modeling is critical for personalized news recommendation. Existing news recommendation methods usually rely on the implicit feedback of users like news clicks to model …
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Fairness-aware News Recommendation with Decomposed Adversarial Learning
2021
News recommendation is important for online news services. Existing news recommendation models are usually learned from users' news click behaviors. Usually the behaviors of users with the same sensitive attributes (e.g., genders) have similar patterns …
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Byzantine-Robust Learning on Heterogeneous Data via Gradient Splitting
2023 · arXiv (Cornell University)
Federated learning has exhibited vulnerabilities to Byzantine attacks, where the Byzantine attackers can send arbitrary gradients to a central server to destroy the convergence and performance of the global model. A wealth of robust AGgregation …
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NPA
2019
News recommendation is very important to help users find interested news and alleviate information overload. Different users usually have different interests and the same user may have various interests. Thus, different users may click the …
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Neural News Recommendation with Long- and Short-term User Representations
2019
Personalized news recommendation is important to help users find their interested news and improve reading experience. A key problem in news recommendation is learning accurate user representations to capture their interests. Users usually have both …
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Exploring Sequence-to-Sequence Learning in Aspect Term Extraction
2019
Aspect term extraction (ATE) aims at identifying all aspect terms in a sentence and is usually modeled as a sequence labeling problem. However, sequence labeling based methods cannot make full use of the overall meaning …
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Neural News Recommendation with Topic-Aware News Representation
2019
News recommendation can help users find interested news and alleviate information overload. The topic information of news is critical for learning accurate news and user representations for news recommendation. However, it is not considered in …
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Neural News Recommendation with Attentive Multi-View Learning
2019
Personalized news recommendation is very important for online news platforms to help users find interested news and improve user experience. News and user representation learning is critical for news recommendation. Existing news recommendation methods usually …
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Reviews Meet Graphs: Enhancing User and Item Representations for Recommendation with Hierarchical Attentive Graph Neural Network
2019
Chuhan Wu, Fangzhao Wu, Tao Qi, Suyu Ge, Yongfeng Huang, Xing Xie. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). …
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Neural News Recommendation with Multi-Head Self-Attention
2019
Chuhan Wu, Fangzhao Wu, Suyu Ge, Tao Qi, Yongfeng Huang, Xing Xie. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). …
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Graph Enhanced Representation Learning for News Recommendation
2020
With the explosion of online news, personalized news recommendation becomes increasingly important for online news platforms to help their users find interesting information. Existing news recommendation methods achieve personalization by building accurate news representations from …
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Fine-grained Interest Matching for Neural News Recommendation
2020
Personalized news recommendation is a critical technology to improve users' online news reading experience. The core of news recommendation is accurate matching between user's interests and candidate news. The same user usually has diverse interests …
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MIND: A Large-scale Dataset for News Recommendation
2020
Fangzhao Wu, Ying Qiao, Jiun-Hung Chen, Chuhan Wu, Tao Qi, Jianxun Lian, Danyang Liu, Xing Xie, Jianfeng Gao, Winnie Wu, Ming Zhou. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 2020.
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Personalized News Recommendation: Methods and Challenges
2022 · ACM Transactions on Information Systems
Personalized news recommendation is important for users to find interesting news information and alleviate information overload. Although it has been extensively studied over decades and has achieved notable success in improving user experience, there are …
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Communication-efficient federated learning via knowledge distillation
2022 · Nature Communications
Federated learning is a privacy-preserving machine learning technique to train intelligent models from decentralized data, which enables exploiting private data by communicating local model updates in each iteration of model learning rather than the raw …