10 citations · 11 across the 2 of their papers we have counts for
4 papers
Low Resource Style Transfer via Domain Adaptive Meta Learning
Xiangyang Li, Xiang Long, Yu Xia +1
Text style transfer (TST) without parallel data has achieved some practical success. However, most of the existing unsupervised text style transfer methods suffer from (i) requirin…
Exploring Text-transformers in AAAI 2021 Shared Task: COVID-19 Fake News Detection in English
Xiangyang Li, Yu Xia, Xiang Long +2
In this paper, we describe our system for the AAAI 2021 shared task of COVID-19 Fake News Detection in English, where we achieved the 3rd position with the weighted F1 score of 0.9…
FenceMask: A Data Augmentation Approach for Pre-extracted Image Features
Pu Li, Xiangyang Li, Xiang Long
We propose a novel data augmentation method named 'FenceMask' that exhibits outstanding performance in various computer vision tasks. It is based on the 'simulation of object occlu…
Review of Text Style Transfer Based on Deep Learning
Xiangyang Li, Guo Pu, Keyu Ming +3
Text style transfer is a hot issue in recent natural language processing,which mainly studies the text to adapt to different specific situations, audiences and purposes by making s…