203 citations · 216 across the 6 of their papers we have counts for
7 papers
Open Rule Induction
Wanyun Cui, Xingran Chen
Rules have a number of desirable properties. It is easy to understand, infer new knowledge, and communicate with other inference systems. One weakness of the previous rule inductio…
Isotonic Data Augmentation for Knowledge Distillation
Wanyun Cui, Sen Yan
Knowledge distillation uses both real hard labels and soft labels predicted by teacher models as supervision. Intuitively, we expect the soft labels and hard labels to be concordan…
Unsupervised Natural Language Inference via Decoupled Multimodal Contrastive Learning
Wanyun Cui, Guangyu Zheng, Wei Wang
We propose to solve the natural language inference problem without any supervision from the inference labels via task-agnostic multimodal pretraining. Although recent studies of mu…
Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement
Zhiqiang Shen, Zhankui He, Wanyun Cui +4
Generic Image recognition is a fundamental and fairly important visual problem in computer vision. One of the major challenges of this task lies in the fact that single image usual…
KBQA: Learning Question Answering over QA Corpora and Knowledge Bases
Wanyun Cui, Yanghua Xiao, Haixun Wang +3
Question answering (QA) has become a popular way for humans to access billion-scale knowledge bases. Unlike web search, QA over a knowledge base gives out accurate and concise resu…
Transfer Learning for Sequences via Learning to Collocate
Wanyun Cui, Guangyu Zheng, Zhiqiang Shen +2
Transfer learning aims to solve the data sparsity for a target domain by applying information of the source domain. Given a sequence (e.g. a natural language sentence), the transfe…