111 citations · 380 across the 48 of their papers we have counts for
7 papers · 2 filters
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…
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…
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…
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…