90 citations · 105 across the 3 of their papers we have counts for
7 papers
Targeted Adversarial Training for Natural Language Understanding
Lis Pereira, Xiaodong Liu, Hao Cheng +3
We present a simple yet effective Targeted Adversarial Training (TAT) algorithm to improve adversarial training for natural language understanding. The key idea is to introspect cu…
Posterior Differential Regularization with f-divergence for Improving Model Robustness
Hao Cheng, Xiaodong Liu, Lis Pereira +2
We address the problem of enhancing model robustness through regularization. Specifically, we focus on methods that regularize the model posterior difference between clean and nois…
Probabilistic Assumptions Matter: Improved Models for Distantly-Supervised Document-Level Question Answering
Hao Cheng, Ming-Wei Chang, Kenton Lee +1
We address the problem of extractive question answering using document-level distant super-vision, pairing questions and relevant documents with answer strings. We compare previous…
Adversarial Training for Large Neural Language Models
Xiaodong Liu, Hao Cheng, Pengcheng He +4
Generalization and robustness are both key desiderata for designing machine learning methods. Adversarial training can enhance robustness, but past work often finds it hurts genera…
The Microsoft Toolkit of Multi-Task Deep Neural Networks for Natural Language Understanding
Xiaodong Liu, Yu Wang, Jianshu Ji +8
We present MT-DNN, an open-source natural language understanding (NLU) toolkit that makes it easy for researchers and developers to train customized deep learning models. Built upo…
Improving Span-based Question Answering Systems with Coarsely Labeled Data
Hao Cheng, Ming-Wei Chang, Kenton Lee +3
We study approaches to improve fine-grained short answer Question Answering models by integrating coarse-grained data annotated for paragraph-level relevance and show that coarsely…