4 citations · 7 across the 3 of their papers we have counts for
6 papers
Generating Fluent Adversarial Examples for Natural Languages
Huangzhao Zhang, Hao Zhou, Ning Miao +1
Efficiently building an adversarial attacker for natural language processing (NLP) tasks is a real challenge. Firstly, as the sentence space is discrete, it is difficult to make sm…
Do You Have the Right Scissors? Tailoring Pre-trained Language Models via Monte-Carlo Methods
Ning Miao, Yuxuan Song, Hao Zhou +1
It has been a common approach to pre-train a language model on a large corpus and fine-tune it on task-specific data. In practice, we observe that fine-tuning a pre-trained model o…
Improving Maximum Likelihood Training for Text Generation with Density Ratio Estimation
Yuxuan Song, Ning Miao, Hao Zhou +3
Auto-regressive sequence generative models trained by Maximum Likelihood Estimation suffer the exposure bias problem in practical finite sample scenarios. The crux is that the numb…
Kernelized Bayesian Softmax for Text Generation
Ning Miao, Hao Zhou, Chengqi Zhao +2
Neural models for text generation require a softmax layer with proper token embeddings during the decoding phase. Most existing approaches adopt single point embedding for each tok…
Dispersed Exponential Family Mixture VAEs for Interpretable Text Generation
Wenxian Shi, Hao Zhou, Ning Miao +1
Deep generative models are commonly used for generating images and text. Interpretability of these models is one important pursuit, other than the generation quality. Variational a…
CGMH: Constrained Sentence Generation by Metropolis-Hastings Sampling
Ning Miao, Hao Zhou, Lili Mou +2
In real-world applications of natural language generation, there are often constraints on the target sentences in addition to fluency and naturalness requirements. Existing languag…