4 citations · 8 across the 3 of their papers we have counts for
3 papers
cs.CL2022★ 4 cited
GENIUS: Sketch-based Language Model Pre-training via Extreme and Selective Masking for Text Generation and Augmentation
Biyang Guo, Yeyun Gong, Yelong Shen +4
We introduce GENIUS: a conditional text generation model using sketches as input, which can fill in the missing contexts for a given sketch (key information consisting of textual s…
cs.CL2021
What Have Been Learned & What Should Be Learned? An Empirical Study of How to Selectively Augment Text for Classification
Biyang Guo, Sonqiao Han, Hailiang Huang
Text augmentation techniques are widely used in text classification problems to improve the performance of classifiers, especially in low-resource scenarios. Whilst lots of creativ…
cs.CL2020★ 4 cited
Label Confusion Learning to Enhance Text Classification Models
Biyang Guo, Songqiao Han, Xiao Han +2
Representing a true label as a one-hot vector is a common practice in training text classification models. However, the one-hot representation may not adequately reflect the relati…