56 citations · 85 across the 5 of their papers we have counts for
5 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…
Topic-Guided Variational Autoencoders for Text Generation
Wenlin Wang, Zhe Gan, Hongteng Xu +5
We propose a topic-guided variational autoencoder (TGVAE) model for text generation. Distinct from existing variational autoencoder (VAE) based approaches, which assume a simple Ga…
Towards Generating Long and Coherent Text with Multi-Level Latent Variable Models
Dinghan Shen, Asli Celikyilmaz, Yizhe Zhang +4
Variational autoencoders (VAEs) have received much attention recently as an end-to-end architecture for text generation with latent variables. In this paper, we investigate several…