activity
20182022
most citedTowards Multi-pose Guided Virtual Try-on Network

17 citations · 44 across the 9 of their papers we have counts for

collaborators

15 papers

cs.LG20221 cited

Optimizing Evaluation Metrics for Multi-Task Learning via the Alternating Direction Method of Multipliers

Ge-Yang Ke, Yan Pan, Jian Yin +1

Multi-task learning (MTL) aims to improve the generalization performance of multiple tasks by exploiting the shared factors among them. Various metrics (e.g., F-score, Area Under t…

cs.CL20216 cited

AR-LSAT: Investigating Analytical Reasoning of Text

Wanjun Zhong, Siyuan Wang, Duyu Tang +6

Analytical reasoning is an essential and challenging task that requires a system to analyze a scenario involving a set of particular circumstances and perform reasoning over it to…

cs.CL2020

Neural Deepfake Detection with Factual Structure of Text

Wanjun Zhong, Duyu Tang, Zenan Xu +5

Deepfake detection, the task of automatically discriminating machine-generated text, is increasingly critical with recent advances in natural language generative models. Existing a…

cs.CL2020

Evidence-Aware Inferential Text Generation with Vector Quantised Variational AutoEncoder

Daya Guo, Duyu Tang, Nan Duan +3

Generating inferential texts about an event in different perspectives requires reasoning over different contexts that the event occurs. Existing works usually ignore the context th…

cs.CL2020

LogicalFactChecker: Leveraging Logical Operations for Fact Checking with Graph Module Network

Wanjun Zhong, Duyu Tang, Zhangyin Feng +7

Verifying the correctness of a textual statement requires not only semantic reasoning about the meaning of words, but also symbolic reasoning about logical operations like count, s…

cs.CL20207 cited

A Heterogeneous Graph with Factual, Temporal and Logical Knowledge for Question Answering Over Dynamic Contexts

Wanjun Zhong, Duyu Tang, Nan Duan +3

We study question answering over a dynamic textual environment. Although neural network models achieve impressive accuracy via learning from input-output examples, they rarely leve…