57 citations · 184 across the 33 of their papers we have counts for
13 papers · 1 filter
An AI-based Solution for Enhancing Delivery of Digital Learning for Future Teachers
Yong-Bin Kang, Abdur Rahim Mohammad Forkan, Prem Prakash Jayaraman +5
There has been a recent and rapid shift to digital learning hastened by the pandemic but also influenced by ubiquitous availability of digital tools and platforms now, making digit…
Towards Graph Self-Supervised Learning with Contrastive Adjusted Zooming
Yizhen Zheng, Ming Jin, Shirui Pan +4
Graph representation learning (GRL) is critical for graph-structured data analysis. However, most of the existing graph neural networks (GNNs) heavily rely on labeling information,…
Simple or Complex? Complexity-Controllable Question Generation with Soft Templates and Deep Mixture of Experts Model
Sheng Bi, Xiya Cheng, Yuan-Fang Li +5
The ability to generate natural-language questions with controlled complexity levels is highly desirable as it further expands the applicability of question generation. In this pap…
Improving Numerical Reasoning Skills in the Modular Approach for Complex Question Answering on Text
Xiao-Yu Guo, Yuan-Fang Li, Gholamreza Haffari
Numerical reasoning skills are essential for complex question answering (CQA) over text. It requires opertaions including counting, comparison, addition and subtraction. A successf…
Semantic Compositional Learning for Low-shot Scene Graph Generation
Tao He, Lianli Gao, Jingkuan Song +2
Scene graphs provide valuable information to many downstream tasks. Many scene graph generation (SGG) models solely use the limited annotated relation triples for training, leading…
Exploiting Scene Graphs for Human-Object Interaction Detection
Tao He, Lianli Gao, Jingkuan Song +1
Human-Object Interaction (HOI) detection is a fundamental visual task aiming at localizing and recognizing interactions between humans and objects. Existing works focus on the visu…
Unsupervised Domain-adaptive Hash for Networks
Tao He, Lianli Gao, Jingkuan Song +1
Abundant real-world data can be naturally represented by large-scale networks, which demands efficient and effective learning algorithms. At the same time, labels may only be avail…
Semi-supervised Network Embedding with Differentiable Deep Quantisation
Tao He, Lianli Gao, Jingkuan Song +1
Learning accurate low-dimensional embeddings for a network is a crucial task as it facilitates many downstream network analytics tasks. For large networks, the trained embeddings o…
XL-Sum: Large-Scale Multilingual Abstractive Summarization for 44 Languages
Tahmid Hasan, Abhik Bhattacharjee, Md Saiful Islam +5
Contemporary works on abstractive text summarization have focused primarily on high-resource languages like English, mostly due to the limited availability of datasets for low/mid-…
Adaptive Knowledge-Enhanced Bayesian Meta-Learning for Few-shot Event Detection
Shirong Shen, Tongtong Wu, Guilin Qi +3
Event detection (ED) aims at detecting event trigger words in sentences and classifying them into specific event types. In real-world applications, ED typically does not have suffi…
Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning
Ming Jin, Yizhen Zheng, Yuan-Fang Li +3
Graph representation learning plays a vital role in processing graph-structured data. However, prior arts on graph representation learning heavily rely on labeling information. To…
Temporal Cascade and Structural Modelling of EHRs for Granular Readmission Prediction
Bhagya Hettige, Weiqing Wang, Yuan-Fang Li +2
Predicting (1) when the next hospital admission occurs and (2) what will happen in the next admission about a patient by mining electronic health record (EHR) data can provide gran…
Curriculum-Meta Learning for Order-Robust Continual Relation Extraction
Tongtong Wu, Xuekai Li, Yuan-Fang Li +4
Continual relation extraction is an important task that focuses on extracting new facts incrementally from unstructured text. Given the sequential arrival order of the relations, t…