Yongfeng Huang

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, …

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 …

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 …

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 …

AdaVAE: Exploring Adaptive GPT-2s in Variational Auto-Encoders for Language Modeling

2022 · arXiv (Cornell University)

Variational Auto-Encoder (VAE) has become the de-facto learning paradigm in achieving representation learning and generation for natural language at the same time. Nevertheless, existing VAE-based language models either employ elementary RNNs, which is not powerful …

MVP-Tuning: Multi-View Knowledge Retrieval with Prompt Tuning for Commonsense Reasoning

2023

Yongfeng Huang, Yanyang Li, Yichong Xu, Lin Zhang, Ruyi Gan, Jiaxing Zhang, Liwei Wang. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2023.

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 …

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 …

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 …

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). …

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). …

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 …

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 …

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 …