335 citations · 1.7k across the 86 of their papers we have counts for
6 papers · 2 filters
Stylized Text Generation Using Wasserstein Autoencoders with a Mixture of Gaussian Prior
Amirpasha Ghabussi, Lili Mou, Olga Vechtomova
Wasserstein autoencoders are effective for text generation. They do not however provide any control over the style and topic of the generated sentences if the dataset has multiple…
Adversarial Learning on the Latent Space for Diverse Dialog Generation
Kashif Khan, Gaurav Sahu, Vikash Balasubramanian +2
Generating relevant responses in a dialog is challenging, and requires not only proper modeling of context in the conversation but also being able to generate fluent sentences duri…
Unsupervised Paraphrasing by Simulated Annealing
Xianggen Liu, Lili Mou, Fandong Meng +3
Unsupervised paraphrase generation is a promising and important research topic in natural language processing. We propose UPSA, a novel approach that accomplishes Unsupervised Para…
Generating Sentences from Disentangled Syntactic and Semantic Spaces
Yu Bao, Hao Zhou, Shujian Huang +5
Variational auto-encoders (VAEs) are widely used in natural language generation due to the regularization of the latent space. However, generating sentences from the continuous lat…
An Imitation Learning Approach to Unsupervised Parsing
Bowen Li, Lili Mou, Frank Keller
Recently, there has been an increasing interest in unsupervised parsers that optimize semantically oriented objectives, typically using reinforcement learning. Unfortunately, the l…
Distilling Task-Specific Knowledge from BERT into Simple Neural Networks
Raphael Tang, Yao Lu, Linqing Liu +3
In the natural language processing literature, neural networks are becoming increasingly deeper and complex. The recent poster child of this trend is the deep language representati…