most citedASTRAL: Adversarial Trained LSTM-CNN for Named Entity Recognition

73 citations · 98 across the 3 of their papers we have counts for

collaborators

5 papers

eess.IV2020

High Quality Remote Sensing Image Super-Resolution Using Deep Memory Connected Network

Wenjia Xu, Guangluan Xu, Yang Wang +3

Single image super-resolution is an effective way to enhance the spatial resolution of remote sensing image, which is crucial for many applications such as target detection and ima…

cs.CV202018 cited

Where is the Model Looking At?--Concentrate and Explain the Network Attention

Wenjia Xu, Jiuniu Wang, Yang Wang +3

Image classification models have achieved satisfactory performance on many datasets, sometimes even better than human. However, The model attention is unclear since the lack of int…

cs.CL20207 cited

SRQA: Synthetic Reader for Factoid Question Answering

Jiuniu Wang, Wenjia Xu, Xingyu Fu +5

The question answering system can answer questions from various fields and forms with deep neural networks, but it still lacks effective ways when facing multiple evidences. We int…

cs.CL202073 cited

ASTRAL: Adversarial Trained LSTM-CNN for Named Entity Recognition

Jiuniu Wang, Wenjia Xu, Xingyu Fu +2

Named Entity Recognition (NER) is a challenging task that extracts named entities from unstructured text data, including news, articles, social comments, etc. The NER system has be…

cs.CL2018

A3Net: Adversarial-and-Attention Network for Machine Reading Comprehension

Jiuniu Wang, Xingyu Fu, Guangluan Xu +4

In this paper, we introduce Adversarial-and-attention Network (A3Net) for Machine Reading Comprehension. This model extends existing approaches from two perspectives. First, advers…