Search Results for author: Zuohui Fu

Found 24 papers, 13 papers with code

Dense Contrastive Visual-Linguistic Pretraining

no code implementations24 Sep 2021 Lei Shi, Kai Shuang, Shijie Geng, Peng Gao, Zuohui Fu, Gerard de Melo, Yunpeng Chen, Sen Su

To overcome these issues, we propose unbiased Dense Contrastive Visual-Linguistic Pretraining (DCVLP), which replaces the region regression and classification with cross-modality region contrastive learning that requires no annotations.

Contrastive Learning Data Augmentation +1

Data Augmentation with Adversarial Training for Cross-Lingual NLI

no code implementations ACL 2021 Xin Dong, Yaxin Zhu, Zuohui Fu, Dongkuan Xu, Gerard de Melo

Due to recent pretrained multilingual representation models, it has become feasible to exploit labeled data from one language to train a cross-lingual model that can then be applied to multiple new languages.

Cross-Lingual Natural Language Inference Data Augmentation

Efficient Non-Sampling Knowledge Graph Embedding

1 code implementation21 Apr 2021 Zelong Li, Jianchao Ji, Zuohui Fu, Yingqiang Ge, Shuyuan Xu, Chong Chen, Yongfeng Zhang

Experiments on benchmark datasets show that our NS-KGE framework can achieve a better performance on efficiency and accuracy over traditional negative sampling based models, and that the framework is applicable to a large class of knowledge graph embedding models.

Knowledge Graph Embedding

User-oriented Fairness in Recommendation

1 code implementation21 Apr 2021 Yunqi Li, Hanxiong Chen, Zuohui Fu, Yingqiang Ge, Yongfeng Zhang

To solve this problem, we provide a re-ranking approach to mitigate this unfairness problem by adding constraints over evaluation metrics.

Fairness Recommendation Systems +1

Context-Aware Interaction Network for Question Matching

no code implementations17 Apr 2021 Zhe Hu, Zuohui Fu, Yu Yin, Gerard de Melo

Impressive milestones have been achieved in text matching by adopting a cross-attention mechanism to capture pertinent semantic connections between two sentence representations.

Text Matching

Faithfully Explainable Recommendation via Neural Logic Reasoning

1 code implementation NAACL 2021 Yaxin Zhu, Yikun Xian, Zuohui Fu, Gerard de Melo, Yongfeng Zhang

Knowledge graphs (KG) have become increasingly important to endow modern recommender systems with the ability to generate traceable reasoning paths to explain the recommendation process.

Decision Making Knowledge Graphs +1

Causal Collaborative Filtering

1 code implementation3 Feb 2021 Shuyuan Xu, Yingqiang Ge, Yunqi Li, Zuohui Fu, Xu Chen, Yongfeng Zhang

In this paper, we propose Causal Collaborative Filtering (CCF) -- a general framework for modeling causality in collaborative filtering and recommendation.

Collaborative Filtering Recommendation Systems

RomeBERT: Robust Training of Multi-Exit BERT

1 code implementation24 Jan 2021 Shijie Geng, Peng Gao, Zuohui Fu, Yongfeng Zhang

In this paper, we leverage gradient regularized self-distillation for RObust training of Multi-Exit BERT (RomeBERT), which can effectively solve the performance imbalance problem between early and late exits.

Natural Language Understanding

Discrete Knowledge Graph Embedding based on Discrete Optimization

no code implementations13 Jan 2021 Yunqi Li, Shuyuan Xu, Bo Liu, Zuohui Fu, Shuchang Liu, Xu Chen, Yongfeng Zhang

This paper proposes a discrete knowledge graph (KG) embedding (DKGE) method, which projects KG entities and relations into the Hamming space based on a computationally tractable discrete optimization algorithm, to solve the formidable storage and computation cost challenges in traditional continuous graph embedding methods.

Knowledge Graph Embedding

CAFE: Coarse-to-Fine Neural Symbolic Reasoning for Explainable Recommendation

1 code implementation29 Oct 2020 Yikun Xian, Zuohui Fu, Handong Zhao, Yingqiang Ge, Xu Chen, Qiaoying Huang, Shijie Geng, Zhou Qin, Gerard de Melo, S. Muthukrishnan, Yongfeng Zhang

User profiles can capture prominent user behaviors from the history, and provide valuable signals about which kinds of path patterns are more likely to lead to potential items of interest for the user.

Knowledge Graphs Recommendation Systems

COOKIE: A Dataset for Conversational Recommendation over Knowledge Graphs in E-commerce

no code implementations21 Aug 2020 Zuohui Fu, Yikun Xian, Yaxin Zhu, Yongfeng Zhang, Gerard de Melo

In this work, we present a new dataset for conversational recommendation over knowledge graphs in e-commerce platforms called COOKIE.

Knowledge Graphs

Leveraging Adversarial Training in Self-Learning for Cross-Lingual Text Classification

no code implementations29 Jul 2020 Xin Dong, Yaxin Zhu, Yupeng Zhang, Zuohui Fu, Dongkuan Xu, Sen yang, Gerard de Melo

The resulting model then serves as a teacher to induce labels for unlabeled target language samples that can be used during further adversarial training, allowing us to gradually adapt our model to the target language.

Classification General Classification +2

Contrastive Visual-Linguistic Pretraining

no code implementations26 Jul 2020 Lei Shi, Kai Shuang, Shijie Geng, Peng Su, Zhengkai Jiang, Peng Gao, Zuohui Fu, Gerard de Melo, Sen Su

We evaluate CVLP on several down-stream tasks, including VQA, GQA and NLVR2 to validate the superiority of contrastive learning on multi-modality representation learning.

Contrastive Learning Representation Learning +1

Neural-Symbolic Reasoning over Knowledge Graph for Multi-stage Explainable Recommendation

no code implementations26 Jul 2020 Yikun Xian, Zuohui Fu, Qiaoying Huang, S. Muthukrishnan, Yongfeng Zhang

Recent work on recommender systems has considered external knowledge graphs as valuable sources of information, not only to produce better recommendations but also to provide explanations of why the recommended items were chosen.

Knowledge Graphs Recommendation Systems

Learning Personalized Risk Preferences for Recommendation

1 code implementation6 Jul 2020 Yingqiang Ge, Shuyuan Xu, Shuchang Liu, Zuohui Fu, Fei Sun, Yongfeng Zhang

Before making purchases on e-commerce websites, most consumers tend to rely on rating scores and review information to make purchase decisions.

Recommendation Systems

Learning Post-Hoc Causal Explanations for Recommendation

no code implementations30 Jun 2020 Shuyuan Xu, Yunqi Li, Shuchang Liu, Zuohui Fu, Xu Chen, Yongfeng Zhang

State-of-the-art recommender systems have the ability to generate high-quality recommendations, but usually cannot provide intuitive explanations to humans due to the usage of black-box prediction models.

Recommendation Systems

Character Matters: Video Story Understanding with Character-Aware Relations

no code implementations9 May 2020 Shijie Geng, Ji Zhang, Zuohui Fu, Peng Gao, Hang Zhang, Gerard de Melo

Without identifying the connection between appearing people and character names, a model is not able to obtain a genuine understanding of the plots.

Question Answering

HID: Hierarchical Multiscale Representation Learning for Information Diffusion

2 code implementations19 Apr 2020 Honglu Zhou, Shuyuan Xu, Zuohui Fu, Gerard de Melo, Yongfeng Zhang, Mubbasir Kapadia

In this paper, we present a Hierarchical Information Diffusion (HID) framework by integrating user representation learning and multiscale modeling.

Representation Learning

ABSent: Cross-Lingual Sentence Representation Mapping with Bidirectional GANs

1 code implementation29 Jan 2020 Zuohui Fu, Yikun Xian, Shijie Geng, Yingqiang Ge, Yuting Wang, Xin Dong, Guang Wang, Gerard de Melo

A number of cross-lingual transfer learning approaches based on neural networks have been proposed for the case when large amounts of parallel text are at our disposal.

Cross-Lingual Transfer Sentence Embedding +1

Reinforcement Knowledge Graph Reasoning for Explainable Recommendation

1 code implementation12 Jun 2019 Yikun Xian, Zuohui Fu, S. Muthukrishnan, Gerard de Melo, Yongfeng Zhang

To this end, we propose a method called Policy-Guided Path Reasoning (PGPR), which couples recommendation and interpretability by providing actual paths in a knowledge graph.

Causal Inference Decision Making +1

OOGAN: Disentangling GAN with One-Hot Sampling and Orthogonal Regularization

1 code implementation26 May 2019 Bingchen Liu, Yizhe Zhu, Zuohui Fu, Gerard de Melo, Ahmed Elgammal

Exploring the potential of GANs for unsupervised disentanglement learning, this paper proposes a novel GAN-based disentanglement framework with One-Hot Sampling and Orthogonal Regularization (OOGAN).

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