71 citations · 101 across the 5 of their papers we have counts for
5 papers
DiscrimLoss: A Universal Loss for Hard Samples and Incorrect Samples Discrimination
Tingting Wu, Xiao Ding, Hao Zhang +4
Given data with label noise (i.e., incorrect data), deep neural networks would gradually memorize the label noise and impair model performance. To relieve this issue, curriculum le…
Text Difficulty Study: Do machines behave the same as humans regarding text difficulty?
Bowen Chen, Xiao Ding, Li Du +2
Given a task, human learns from easy to hard, whereas the model learns randomly. Undeniably, difficulty insensitive learning leads to great success in NLP, but little attention has…
A Graph Enhanced BERT Model for Event Prediction
Li Du, Xiao Ding, Yue Zhang +3
Predicting the subsequent event for an existing event context is an important but challenging task, as it requires understanding the underlying relationship between events. Previou…
Constructing Narrative Event Evolutionary Graph for Script Event Prediction
Zhongyang Li, Xiao Ding, Ting Liu
Script event prediction requires a model to predict the subsequent event given an existing event context. Previous models based on event pairs or event chains cannot make full use…
A General Framework for Content-enhanced Network Representation Learning
Xiaofei Sun, Jiang Guo, Xiao Ding +1
This paper investigates the problem of network embedding, which aims at learning low-dimensional vector representation of nodes in networks. Most existing network embedding methods…