Search Results for author: JianXin Li

Found 47 papers, 25 papers with code

Noise-injected Consistency Training and Entropy-constrained Pseudo Labeling for Semi-supervised Extractive Summarization

1 code implementation COLING 2022 Yiming Wang, Qianren Mao, Junnan Liu, Weifeng Jiang, Hongdong Zhu, JianXin Li

Labeling large amounts of extractive summarization data is often prohibitive expensive due to time, financial, and expertise constraints, which poses great challenges to incorporating summarization system in practical applications.

Extractive Summarization

Pseudo-Label Guided Unsupervised Domain Adaptation of Contextual Embeddings

no code implementations EACL (AdaptNLP) 2021 Tianyu Chen, Shaohan Huang, Furu Wei, JianXin Li

In unsupervised domain adaptation, we aim to train a model that works well on a target domain when provided with labeled source samples and unlabeled target samples.

Language Modelling Masked Language Modeling +3

A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPT

no code implementations18 Feb 2023 Ce Zhou, Qian Li, Chen Li, Jun Yu, Yixin Liu, Guangjing Wang, Kai Zhang, Cheng Ji, Qiben Yan, Lifang He, Hao Peng, JianXin Li, Jia Wu, Ziwei Liu, Pengtao Xie, Caiming Xiong, Jian Pei, Philip S. Yu, Lichao Sun

This study provides a comprehensive review of recent research advancements, current and future challenges, and opportunities for PFMs in text, image, graph, as well as other data modalities.

Graph Learning Language Modelling

Unbiased and Efficient Self-Supervised Incremental Contrastive Learning

1 code implementation28 Jan 2023 Cheng Ji, JianXin Li, Hao Peng, Jia Wu, Xingcheng Fu, Qingyun Sun, Phillip S. Yu

Contrastive Learning (CL) has been proved to be a powerful self-supervised approach for a wide range of domains, including computer vision and graph representation learning.

Contrastive Learning Graph Representation Learning +1

Self-organization Preserved Graph Structure Learning with Principle of Relevant Information

no code implementations30 Dec 2022 Qingyun Sun, JianXin Li, Beining Yang, Xingcheng Fu, Hao Peng, Philip S. Yu

Most Graph Neural Networks follow the message-passing paradigm, assuming the observed structure depicts the ground-truth node relationships.

Graph structure learning

Type Information Utilized Event Detection via Multi-Channel GNNs in Electrical Power Systems

no code implementations15 Nov 2022 Qian Li, JianXin Li, Lihong Wang, Cheng Ji, Yiming Hei, Jiawei Sheng, Qingyun Sun, Shan Xue, Pengtao Xie

To address the above issues, we propose a Multi-Channel graph neural network utilizing Type information for Event Detection in power systems, named MC-TED, leveraging a semantic channel and a topological channel to enrich information interaction from short texts.

Event Detection Semantic Similarity +1

Position-aware Structure Learning for Graph Topology-imbalance by Relieving Under-reaching and Over-squashing

1 code implementation17 Aug 2022 Qingyun Sun, JianXin Li, Haonan Yuan, Xingcheng Fu, Hao Peng, Cheng Ji, Qian Li, Philip S. Yu

Topology-imbalance is a graph-specific imbalance problem caused by the uneven topology positions of labeled nodes, which significantly damages the performance of GNNs.

Graph Learning Graph structure learning +1

THE-X: Privacy-Preserving Transformer Inference with Homomorphic Encryption

no code implementations Findings (ACL) 2022 Tianyu Chen, Hangbo Bao, Shaohan Huang, Li Dong, Binxing Jiao, Daxin Jiang, Haoyi Zhou, JianXin Li, Furu Wei

As more and more pre-trained language models adopt on-cloud deployment, the privacy issues grow quickly, mainly for the exposure of plain-text user data (e. g., search history, medical record, bank account).

Privacy Preserving

Task-Specific Expert Pruning for Sparse Mixture-of-Experts

no code implementations1 Jun 2022 Tianyu Chen, Shaohan Huang, Yuan Xie, Binxing Jiao, Daxin Jiang, Haoyi Zhou, JianXin Li, Furu Wei

The sparse Mixture-of-Experts (MoE) model is powerful for large-scale pre-training and has achieved promising results due to its model capacity.

MTTrans: Cross-Domain Object Detection with Mean-Teacher Transformer

1 code implementation3 May 2022 Jinze Yu, Jiaming Liu, Xiaobao Wei, Haoyi Zhou, Yohei Nakata, Denis Gudovskiy, Tomoyuki Okuno, JianXin Li, Kurt Keutzer, Shanghang Zhang

To solve this problem, we propose an end-to-end cross-domain detection Transformer based on the mean teacher framework, MTTrans, which can fully exploit unlabeled target domain data in object detection training and transfer knowledge between domains via pseudo labels.

Domain Adaptation object-detection +2

Curvature Graph Generative Adversarial Networks

1 code implementation3 Mar 2022 JianXin Li, Xingcheng Fu, Qingyun Sun, Cheng Ji, Jiajun Tan, Jia Wu, Hao Peng

In this paper, we proposed a novel Curvature Graph Generative Adversarial Networks method, named \textbf{\modelname}, which is the first GAN-based graph representation method in the Riemannian geometric manifold.

Exploring Human Mobility for Multi-Pattern Passenger Prediction: A Graph Learning Framework

no code implementations17 Feb 2022 Xiangjie Kong, Kailai Wang, Mingliang Hou, Feng Xia, Gour Karmakar, JianXin Li

To reduce this research gap and learn human mobility knowledge from this fixed travel behaviors, we propose a multi-pattern passenger flow prediction framework, MPGCN, based on Graph Convolutional Network (GCN).

Deep Clustering Graph Learning

Network resilience in the aging brain

no code implementations3 Feb 2022 Tao Liu, Shu Guo, Hao liu, Rui Kang, Mingyang Bai, Jiyang Jiang, Wei Wen, Xing Pan, Jun Tai, JianXin Li, Jian Cheng, Jing Jing, Zhenzhou Wu, Haijun Niu, Haogang Zhu, Zixiao Li, Yongjun Wang, Henry Brodaty, Perminder Sachdev, Daqing Li

Degeneration and adaptation are two competing sides of the same coin called resilience in the progressive processes of brain aging or diseases.

Bi-CLKT: Bi-Graph Contrastive Learning based Knowledge Tracing

no code implementations22 Jan 2022 XiangYu Song, JianXin Li, Qi Lei, Wei Zhao, Yunliang Chen, Ajmal Mian

The goal of Knowledge Tracing (KT) is to estimate how well students have mastered a concept based on their historical learning of related exercises.

Contrastive Learning Knowledge Tracing +1

Temporal Knowledge Graph Completion: A Survey

no code implementations16 Jan 2022 Borui Cai, Yong Xiang, Longxiang Gao, He Zhang, Yunfeng Li, JianXin Li

KGC methods assume a knowledge graph is static, but that may lead to inaccurate prediction results because many facts in the knowledge graphs change over time.

Temporal Knowledge Graph Completion

Graph Structure Learning with Variational Information Bottleneck

1 code implementation16 Dec 2021 Qingyun Sun, JianXin Li, Hao Peng, Jia Wu, Xingcheng Fu, Cheng Ji, Philip S. Yu

Graph Neural Networks (GNNs) have shown promising results on a broad spectrum of applications.

Graph structure learning

POLLA: Enhancing the Local Structure Awareness in Long Sequence Spatial-temporal Modeling

1 code implementation TIST 2021 2021 Haoyi Zhou, Hao Peng, Jieqi Peng, Shuai Zhang, JianXin Li

Extensive experiments are conducted on five large-scale datasets, which demonstrate that our method achieves state-of-the-art performance and validates the effectiveness brought by local structure information.

ACE-HGNN: Adaptive Curvature Exploration Hyperbolic Graph Neural Network

1 code implementation15 Oct 2021 Xingcheng Fu, JianXin Li, Jia Wu, Qingyun Sun, Cheng Ji, Senzhang Wang, Jiajun Tan, Hao Peng, Philip S. Yu

Hyperbolic Graph Neural Networks(HGNNs) extend GNNs to hyperbolic space and thus are more effective to capture the hierarchical structures of graphs in node representation learning.

Graph Learning Multi-agent Reinforcement Learning +1

Sentiment Analysis and Topic Modeling for COVID-19 Vaccine Discussions

no code implementations8 Oct 2021 Hui Yin, XiangYu Song, Shuiqiao Yang, JianXin Li

The outbreak of the novel Coronavirus Disease 2019 (COVID-19) has lasted for nearly two years and caused unprecedented impacts on people's daily life around the world.

Sentiment Analysis

Distributed Optimization of Graph Convolutional Network using Subgraph Variance

no code implementations6 Oct 2021 Taige Zhao, XiangYu Song, JianXin Li, Wei Luo, Imran Razzak

We first propose a graph augmentation-based partition (GAD-Partition) that can divide original graph into augmented subgraphs to reduce communication by selecting and storing as few significant nodes of other processors as possible while guaranteeing the accuracy of the training.

Distributed Optimization

Gradient Broadcast Adaptation: Defending against the backdoor attack in pre-trained models

no code implementations29 Sep 2021 Tianyu Chen, Haoyi Zhou, He Mingrui, JianXin Li

Pre-trained language models (e. g, BERT, GPT-3) have revolutionized the NLP research and fine-tuning becomes the indispensable step of downstream adaptation.

Backdoor Attack text-classification +1

Representation Learning for Short Text Clustering

no code implementations21 Sep 2021 Hui Yin, XiangYu Song, Shuiqiao Yang, Guangyan Huang, JianXin Li

Effective representation learning is critical for short text clustering due to the sparse, high-dimensional and noise attributes of short text corpus.

Representation Learning Short Text Clustering

Event Extraction by Associating Event Types and Argument Roles

no code implementations23 Aug 2021 Qian Li, Shu Guo, Jia Wu, JianXin Li, Jiawei Sheng, Lihong Wang, Xiaohan Dong, Hao Peng

It ignores meaningful associations among event types and argument roles, leading to relatively poor performance for less frequent types/roles.

Association Event Extraction +2

A Survey on Deep Learning Event Extraction: Approaches and Applications

no code implementations5 Jul 2021 Qian Li, JianXin Li, Jiawei Sheng, Shiyao Cui, Jia Wu, Yiming Hei, Hao Peng, Shu Guo, Lihong Wang, Amin Beheshti, Philip S. Yu

Numerous methods, datasets, and evaluation metrics have been proposed in the literature, raising the need for a comprehensive and updated survey.

Event Extraction

Reinforcement Learning-based Dialogue Guided Event Extraction to Exploit Argument Relations

1 code implementation23 Jun 2021 Qian Li, Hao Peng, JianXin Li, Jia Wu, Yuanxing Ning, Lihong Wang, Philip S. Yu, Zheng Wang

Our approach leverages knowledge of the already extracted arguments of the same sentence to determine the role of arguments that would be difficult to decide individually.

Event Extraction Incremental Learning +2

RoSearch: Search for Robust Student Architectures When Distilling Pre-trained Language Models

no code implementations7 Jun 2021 Xin Guo, Jianlei Yang, Haoyi Zhou, Xucheng Ye, JianXin Li

In order to overcome these security problems, RoSearch is proposed as a comprehensive framework to search the student models with better adversarial robustness when performing knowledge distillation.

Adversarial Robustness Knowledge Distillation +1

Attend and select: A segment selective transformer for microblog hashtag generation

1 code implementation6 Jun 2021 Qianren Mao, Xi Li, Bang Liu, Shu Guo, Peng Hao, JianXin Li, Lihong Wang

These tokens or phrases may originate from primary fragmental textual pieces (e. g., segments) in the original text and are separated into different segments.

Automated Timeline Length Selection for Flexible Timeline Summarization

no code implementations29 May 2021 Xi Li, Qianren Mao, Hao Peng, Hongdong Zhu, JianXin Li, Zheng Wang

This paper presents a better TLS approach for automatically and dynamically determining the TLS timeline length.

Information Retrieval Retrieval +2

Noised Consistency Training for Text Summarization

no code implementations28 May 2021 Junnan Liu, Qianren Mao, Bang Liu, Hao Peng, Hongdong Zhu, JianXin Li

In this paper, we argue that this limitation can be overcome by a semi-supervised approach: consistency training which is to leverage large amounts of unlabeled data to improve the performance of supervised learning over a small corpus.

Abstractive Text Summarization

A Robust and Generalized Framework for Adversarial Graph Embedding

1 code implementation22 May 2021 JianXin Li, Xingcheng Fu, Hao Peng, Senzhang Wang, Shijie Zhu, Qingyun Sun, Philip S. Yu, Lifang He

With the prevalence of graph data in real-world applications, many methods have been proposed in recent years to learn high-quality graph embedding vectors various types of graphs.

Graph Embedding Graph Mining +3

Differentially Private Federated Knowledge Graphs Embedding

1 code implementation17 May 2021 Hao Peng, Haoran Li, Yangqiu Song, Vincent Zheng, JianXin Li

However, for multiple cross-domain knowledge graphs, state-of-the-art embedding models cannot make full use of the data from different knowledge domains while preserving the privacy of exchanged data.

Knowledge Graph Embedding Knowledge Graphs +3

Higher-Order Attribute-Enhancing Heterogeneous Graph Neural Networks

1 code implementation16 Apr 2021 JianXin Li, Hao Peng, Yuwei Cao, Yingtong Dou, Hekai Zhang, Philip S. Yu, Lifang He

Furthermore, they cannot fully capture the content-based correlations between nodes, as they either do not use the self-attention mechanism or only use it to consider the immediate neighbors of each node, ignoring the higher-order neighbors.

Node Classification Node Clustering +1

HTCInfoMax: A Global Model for Hierarchical Text Classification via Information Maximization

1 code implementation NAACL 2021 Zhongfen Deng, Hao Peng, Dongxiao He, JianXin Li, Philip S. Yu

The second one encourages the structure encoder to learn better representations with desired characteristics for all labels which can better handle label imbalance in hierarchical text classification.

General Classification Representation Learning +2

Streaming Social Event Detection and Evolution Discovery in Heterogeneous Information Networks

1 code implementation2 Apr 2021 Hao Peng, JianXin Li, Yangqiu Song, Renyu Yang, Rajiv Ranjan, Philip S. Yu, Lifang He

Third, we propose a streaming social event detection and evolution discovery framework for HINs based on meta-path similarity search, historical information about meta-paths, and heterogeneous DBSCAN clustering method.

Event Detection

Dynamic Network Embedding Survey

no code implementations29 Mar 2021 Guotong Xue, Ming Zhong, JianXin Li, Jia Chen, Chengshuai Zhai, Ruochen Kong

Due to the lack of comprehensive investigation of them, we give a survey of dynamic network embedding in this paper.

Network Embedding

Knowledge-Preserving Incremental Social Event Detection via Heterogeneous GNNs

2 code implementations21 Jan 2021 Yuwei Cao, Hao Peng, Jia Wu, Yingtong Dou, JianXin Li, Philip S. Yu

The complexity and streaming nature of social messages make it appealing to address social event detection in an incremental learning setting, where acquiring, preserving, and extending knowledge are major concerns.

Event Detection Feature Engineering +4

Chaotic-to-Fine Clustering for Unlabeled Plant Disease Images

no code implementations18 Jan 2021 Uno Fang, JianXin Li, Xuequan Lu, Mumtaz Ali, Longxiang Gao, Yong Xiang

Current annotation for plant disease images depends on manual sorting and handcrafted features by agricultural experts, which is time-consuming and labour-intensive.

Hierarchical Bi-Directional Self-Attention Networks for Paper Review Rating Recommendation

1 code implementation COLING 2020 Zhongfen Deng, Hao Peng, Congying Xia, JianXin Li, Lifang He, Philip S. Yu

Review rating prediction of text reviews is a rapidly growing technology with a wide range of applications in natural language processing.

Decision Making

Adversarial Directed Graph Embedding

1 code implementation9 Aug 2020 Shijie Zhu, JianXin Li, Hao Peng, Senzhang Wang, Lifang He

To capture the directed edges between nodes, existing methods mostly learn two embedding vectors for each node, source vector and target vector.

Graph Embedding Graph Mining

RWNE: A Scalable Random-Walk-Based Network Embedding Framework with Personalized Higher-Order Proximity Preserved

1 code implementation18 Nov 2019 JianXin Li, Cheng Ji, Hao Peng, Yu He, Yangqiu Song, Xinmiao Zhang, Fanzhang Peng

However, despite the success of current random-walk-based methods, most of them are usually not expressive enough to preserve the personalized higher-order proximity and lack a straightforward objective to theoretically articulate what and how network proximity is preserved.

Network Embedding

A Lightweight Music Texture Transfer System

1 code implementation arXiv preprint 2018 Xutan Peng, Chen Li, Zhi Cai, Faqiang Shi, Yidan Liu, JianXin Li

In this paper, we initiate a novel system for transferring the texture of music, and release it as an open source project.

Music Texture Transfer

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