124 citations · 176 across the 5 of their papers we have counts for
6 papers
Learning Compressed Sentence Representations for On-Device Text Processing
Dinghan Shen, Pengyu Cheng, Dhanasekar Sundararaman +5
Vector representations of sentences, trained on massive text corpora, are widely used as generic sentence embeddings across a variety of NLP problems. The learned representations a…
Syntax-Infused Variational Autoencoder for Text Generation
Xinyuan Zhang, Yi Yang, Siyang Yuan +2
We present a syntax-infused variational autoencoder (SIVAE), that integrates sentences with their syntactic trees to improve the grammar of generated sentences. Distinct from exist…
Improving Textual Network Embedding with Global Attention via Optimal Transport
Liqun Chen, Guoyin Wang, Chenyang Tao +6
Constituting highly informative network embeddings is an important tool for network analysis. It encodes network topology, along with other useful side information, into low-dimens…
Video Generation From Text
Yitong Li, Martin Renqiang Min, Dinghan Shen +2
Generating videos from text has proven to be a significant challenge for existing generative models. We tackle this problem by training a conditional generative model to extract bo…
Deconvolutional Latent-Variable Model for Text Sequence Matching
Dinghan Shen, Yizhe Zhang, Ricardo Henao +2
A latent-variable model is introduced for text matching, inferring sentence representations by jointly optimizing generative and discriminative objectives. To alleviate typical opt…
Adversarial Feature Matching for Text Generation
Yizhe Zhang, Zhe Gan, Kai Fan +4
The Generative Adversarial Network (GAN) has achieved great success in generating realistic (real-valued) synthetic data. However, convergence issues and difficulties dealing with…