31 citations · 116 across the 14 of their papers we have counts for
6 papers · 2 filters
Searching for an Effective Defender: Benchmarking Defense against Adversarial Word Substitution
Zongyi Li, Jianhan Xu, Jiehang Zeng +5
Recent studies have shown that deep neural networks are vulnerable to intentionally crafted adversarial examples, and various methods have been proposed to defend against adversari…
Exploration and Exploitation: Two Ways to Improve Chinese Spelling Correction Models
Chong Li, Cenyuan Zhang, Xiaoqing Zheng +1
A sequence-to-sequence learning with neural networks has empirically proven to be an effective framework for Chinese Spelling Correction (CSC), which takes a sentence with some spe…
Certified Robustness to Text Adversarial Attacks by Randomized [MASK]
Jiehang Zeng, Xiaoqing Zheng, Jianhan Xu +3
Recently, few certified defense methods have been developed to provably guarantee the robustness of a text classifier to adversarial synonym substitutions. However, all existing ce…
Alleviate Exposure Bias in Sequence Prediction \\ with Recurrent Neural Networks
Liping Yuan, Jiangtao Feng, Xiaoqing Zheng +1
A popular strategy to train recurrent neural networks (RNNs), known as ``teacher forcing'' takes the ground truth as input at each time step and makes the later predictions partly…
Unsupervised Word Segmentation with Bi-directional Neural Language Model
Lihao Wang, Zongyi Li, Xiaoqing Zheng
We present an unsupervised word segmentation model, in which the learning objective is to maximize the generation probability of a sentence given its all possible segmentation. Suc…
SparseGAN: Sparse Generative Adversarial Network for Text Generation
Liping Yuan, Jiehang Zeng, Xiaoqing Zheng
It is still a challenging task to learn a neural text generation model under the framework of generative adversarial networks (GANs) since the entire training process is not differ…