217 citations · 2.1k across the 107 of their papers we have counts for
110 papers
SkillQG: Learning to Generate Question for Reading Comprehension Assessment
Xiaoqiang Wang, Bang Liu, Siliang Tang +1
We present $\textbf{$\texttt{SkillQG}$}$: a question generation framework with controllable comprehension types for assessing and improving machine reading comprehension models. Ex…
Tram: A Token-level Retrieval-augmented Mechanism for Source Code Summarization
Tong Ye, Lingfei Wu, Tengfei Ma +5
Automatically generating human-readable text describing the functionality of a program is the intent of source code summarization. Although neural language models achieve significa…
KGTrust: Evaluating Trustworthiness of SIoT via Knowledge Enhanced Graph Neural Networks
Zhizhi Yu, Di Jin, Cuiying Huo +5
Social Internet of Things (SIoT), a promising and emerging paradigm that injects the notion of social networking into smart objects (i.e., things), paving the way for the next gene…
Pruning Before Training May Improve Generalization, Provably
Hongru Yang, Yingbin Liang, Xiaojie Guo +2
It has been observed in practice that applying pruning-at-initialization methods to neural networks and training the sparsified networks can not only retain the testing performance…
T2-GNN: Graph Neural Networks for Graphs with Incomplete Features and Structure via Teacher-Student Distillation
Cuiying Huo, Di Jin, Yawen Li +3
Graph Neural Networks (GNNs) have been a prevailing technique for tackling various analysis tasks on graph data. A key premise for the remarkable performance of GNNs relies on comp…
Human-instructed Deep Hierarchical Generative Learning for Automated Urban Planning
Dongjie Wang, Lingfei Wu, Denghui Zhang +3
The essential task of urban planning is to generate the optimal land-use configuration of a target area. However, traditional urban planning is time-consuming and labor-intensive.…
HigeNet: A Highly Efficient Modeling for Long Sequence Time Series Prediction in AIOps
Jiajia Li, Feng Tan, Cheng He +4
Modern IT system operation demands the integration of system software and hardware metrics. As a result, it generates a massive amount of data, which can be potentially used to mak…
Automatic Scene-based Topic Channel Construction System for E-Commerce
Peng Lin, Yanyan Zou, Lingfei Wu +3
Scene marketing that well demonstrates user interests within a certain scenario has proved effective for offline shopping. To conduct scene marketing for e-commerce platforms, this…
Quantifying hierarchy in scientific teams
Fengli Xu, Lingfei Wu, James A. Evans
This paper provides a detailed description of the data collection and machine learning model used in our recent PNAS paper "Flat Teams Drive Scientific Innovation" Xu et al. [2022a…
Knowledge Distillation based Contextual Relevance Matching for E-commerce Product Search
Ziyang Liu, Chaokun Wang, Hao Feng +2
Online relevance matching is an essential task of e-commerce product search to boost the utility of search engines and ensure a smooth user experience. Previous work adopts either…
Automatic Generation of Product-Image Sequence in E-commerce
Xiaochuan Fan, Chi Zhang, Yong Yang +6
Product images are essential for providing desirable user experience in an e-commerce platform. For a platform with billions of products, it is extremely time-costly and labor-expe…
Input-agnostic Certified Group Fairness via Gaussian Parameter Smoothing
Jiayin Jin, Zeru Zhang, Yang Zhou +1
Only recently, researchers attempt to provide classification algorithms with provable group fairness guarantees. Most of these algorithms suffer from harassment caused by the requi…
Automatic Controllable Product Copywriting for E-Commerce
Xiaojie Guo, Qingkai Zeng, Meng Jiang +3
Automatic product description generation for e-commerce has witnessed significant advancement in the past decade. Product copywriting aims to attract users' interest and improve us…
TeKo: Text-Rich Graph Neural Networks with External Knowledge
Zhizhi Yu, Di Jin, Jianguo Wei +5
Graph Neural Networks (GNNs) have gained great popularity in tackling various analytical tasks on graph-structured data (i.e., networks). Typical GNNs and their variants follow a m…
Robust Meta-learning with Sampling Noise and Label Noise via Eigen-Reptile
Dong Chen, Lingfei Wu, Siliang Tang +3
Recent years have seen a surge of interest in meta-learning techniques for tackling the few-shot learning (FSL) problem. However, the meta-learner is prone to overfitting since the…
Remote Collaboration Fuses Fewer Breakthrough Ideas
Yiling Lin, Carl Benedikt Frey, Lingfei Wu
Theories of innovation emphasize the role of social networks and teams as facilitators of breakthrough discoveries. Around the world, scientists and inventors today are more plenti…
TrustGNN: Graph Neural Network based Trust Evaluation via Learnable Propagative and Composable Nature
Cuiying Huo, Di Jin, Chundong Liang +3
Trust evaluation is critical for many applications such as cyber security, social communication and recommender systems. Users and trust relationships among them can be seen as a g…
Scenario-based Multi-product Advertising Copywriting Generation for E-Commerce
Xueying Zhang, Kai Shen, Chi Zhang +5
In this paper, we proposed an automatic Scenario-based Multi-product Advertising Copywriting Generation system (SMPACG) for E-Commerce, which has been deployed on a leading Chinese…
Improving Long Tailed Document-Level Relation Extraction via Easy Relation Augmentation and Contrastive Learning
Yangkai Du, Tengfei Ma, Lingfei Wu +4
Towards real-world information extraction scenario, research of relation extraction is advancing to document-level relation extraction(DocRE). Existing approaches for DocRE aim to…
Meta Policy Learning for Cold-Start Conversational Recommendation
Zhendong Chu, Hongning Wang, Yun Xiao +2
Conversational recommender systems (CRS) explicitly solicit users' preferences for improved recommendations on the fly. Most existing CRS solutions count on a single policy trained…
Heterogeneous Graph Neural Networks using Self-supervised Reciprocally Contrastive Learning
Cuiying Huo, Dongxiao He, Yawen Li +5
Heterogeneous graph neural network (HGNN) is a very popular technique for the modeling and analysis of heterogeneous graphs. Most existing HGNN-based approaches are supervised or s…
QRelScore: Better Evaluating Generated Questions with Deeper Understanding of Context-aware Relevance
Xiaoqiang Wang, Bang Liu, Siliang Tang +1
Existing metrics for assessing question generation not only require costly human reference but also fail to take into account the input context of generation, rendering the lack of…
Knowledge-aware Document Summarization: A Survey of Knowledge, Embedding Methods and Architectures
Yutong Qu, Wei Emma Zhang, Jian Yang +2
Knowledge-aware methods have boosted a range of natural language processing applications over the last decades. With the gathered momentum, knowledge recently has been pumped into…
Feeding What You Need by Understanding What You Learned
Xiaoqiang Wang, Bang Liu, Fangli Xu +3
Machine Reading Comprehension (MRC) reveals the ability to understand a given text passage and answer questions based on it. Existing research works in MRC rely heavily on large-si…