750 citations · 916 across the 5 of their papers we have counts for
5 papers
On the Convergence and Robustness of Adversarial Training
Yisen Wang, Xingjun Ma, James Bailey +3
Improving the robustness of deep neural networks (DNNs) to adversarial examples is an important yet challenging problem for secure deep learning. Across existing defense techniques…
Neural Models for Sequence Chunking
Feifei Zhai, Saloni Potdar, Bing Xiang +1
Many natural language understanding (NLU) tasks, such as shallow parsing (i.e., text chunking) and semantic slot filling, require the assignment of representative labels to the mea…
Classify or Select: Neural Architectures for Extractive Document Summarization
Ramesh Nallapati, Bowen Zhou, Mingbo Ma
We present two novel and contrasting Recurrent Neural Network (RNN) based architectures for extractive summarization of documents. The Classifier based architecture sequentially ac…
SummaRuNNer: A Recurrent Neural Network based Sequence Model for Extractive Summarization of Documents
Ramesh Nallapati, Feifei Zhai, Bowen Zhou
We present SummaRuNNer, a Recurrent Neural Network (RNN) based sequence model for extractive summarization of documents and show that it achieves performance better than or compara…
End-to-End Answer Chunk Extraction and Ranking for Reading Comprehension
Yang Yu, Wei Zhang, Kazi Hasan +3
This paper proposes dynamic chunk reader (DCR), an end-to-end neural reading comprehension (RC) model that is able to extract and rank a set of answer candidates from a given docum…