111 citations · 380 across the 48 of their papers we have counts for
15 papers · 1 filter
AdvExpander: Generating Natural Language Adversarial Examples by Expanding Text
Zhihong Shao, Zitao Liu, Jiyong Zhang +2
Adversarial examples are vital to expose the vulnerability of machine learning models. Despite the success of the most popular substitution-based methods which substitutes some cha…
Node Similarity Preserving Graph Convolutional Networks
Wei Jin, Tyler Derr, Yiqi Wang +3
Graph Neural Networks (GNNs) have achieved tremendous success in various real-world applications due to their strong ability in graph representation learning. GNNs explore the grap…
Personalized Multimodal Feedback Generation in Education
Haochen Liu, Zitao Liu, Zhongqin Wu +1
The automatic evaluation for school assignments is an important application of AI in the education field. In this work, we focus on the task of personalized multimodal feedback gen…
Mitigating Gender Bias for Neural Dialogue Generation with Adversarial Learning
Haochen Liu, Wentao Wang, Yiqi Wang +3
Dialogue systems play an increasingly important role in various aspects of our daily life. It is evident from recent research that dialogue systems trained on human conversation da…
Mathematical Word Problem Generation from Commonsense Knowledge Graph and Equations
Tianqiao Liu, Qiang Fang, Wenbiao Ding +3
There is an increasing interest in the use of mathematical word problem (MWP) generation in educational assessment. Different from standard natural question generation, MWP generat…
Representation Learning from Limited Educational Data with Crowdsourced Labels
Wentao Wang, Guowei Xu, Wenbiao Ding +4
Representation learning has been proven to play an important role in the unprecedented success of machine learning models in numerous tasks, such as machine translation, face recog…
Contrastive Clustering
Yunfan Li, Peng Hu, Zitao Liu +3
In this paper, we propose a one-stage online clustering method called Contrastive Clustering (CC) which explicitly performs the instance- and cluster-level contrastive learning. To…
Self-supervised Learning on Graphs: Deep Insights and New Direction
Wei Jin, Tyler Derr, Haochen Liu +4
The success of deep learning notoriously requires larger amounts of costly annotated data. This has led to the development of self-supervised learning (SSL) that aims to alleviate…
Automatic Dialogic Instruction Detection for K-12 Online One-on-one Classes
Shiting Xu, Wenbiao Ding, Zitao Liu
Online one-on-one class is created for highly interactive and immersive learning experience. It demands a large number of qualified online instructors. In this work, we develop six…
Neural Multi-Task Learning for Teacher Question Detection in Online Classrooms
Gale Yan Huang, Jiahao Chen, Haochen Liu +6
Asking questions is one of the most crucial pedagogical techniques used by teachers in class. It not only offers open-ended discussions between teachers and students to exchange id…
Siamese Neural Networks for Class Activity Detection
Hang Li, Zhiwei Wang, Jiliang Tang +2
Classroom activity detection (CAD) aims at accurately recognizing speaker roles (either teacher or student) in classrooms. A CAD solution helps teachers get instant feedback on the…
Synchronous Bidirectional Learning for Multilingual Lip Reading
Mingshuang Luo, Shuang Yang, Xilin Chen +2
Lip reading has received increasing attention in recent years. This paper focuses on the synergy of multilingual lip reading. There are about as many as 7000 languages in the world…
Learning Goal-oriented Dialogue Policy with Opposite Agent Awareness
Zheng Zhang, Lizi Liao, Xiaoyan Zhu +4
Most existing approaches for goal-oriented dialogue policy learning used reinforcement learning, which focuses on the target agent policy and simply treat the opposite agent policy…
Identifying At-Risk K-12 Students in Multimodal Online Environments: A Machine Learning Approach
Hang Li, Wenbiao Ding, Zitao Liu
With the rapid emergence of K-12 online learning platforms, a new era of education has been opened up. It is crucial to have a dropout warning framework to preemptively identify K-…
NeuCrowd: Neural Sampling Network for Representation Learning with Crowdsourced Labels
Yang Hao, Wenbiao Ding, Zitao Liu
Representation learning approaches require a massive amount of discriminative training data, which is unavailable in many scenarios, such as healthcare, smart city, education, etc.…