4 citations · 7 across the 5 of their papers we have counts for
5 papers
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…
IDEA: Interactive DoublE Attentions from Label Embedding for Text Classification
Ziyuan Wang, Hailiang Huang, Songqiao Han
Current text classification methods typically encode the text merely into embedding before a naive or complicated classifier, which ignores the suggestive information contained in…
American Hate Crime Trends Prediction with Event Extraction
Songqiao Han, Hailiang Huang, Jiangwei Liu +1
Social media platforms may provide potential space for discourses that contain hate speech, and even worse, can act as a propagation mechanism for hate crimes. The FBI's Uniform Cr…
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…
CreditPrint: Credit Investigation via Geographic Footprints by Deep Learning
Xiao Han, Ruiqing Ding, Leye Wang +1
Credit investigation is critical for financial services. Whereas, traditional methods are often restricted as the employed data hardly provide sufficient, timely and reliable infor…