100 citations · 438 across the 31 of their papers we have counts for
10 papers · 1 filter
A Tutorial on Deep Latent Variable Models of Natural Language
Yoon Kim, Sam Wiseman, Alexander M. Rush
There has been much recent, exciting work on combining the complementary strengths of latent variable models and deep learning. Latent variable modeling makes it easy to explicitly…
End-to-End Content and Plan Selection for Data-to-Text Generation
Sebastian Gehrmann, Falcon Z. Dai, Henry Elder +1
Learning to generate fluent natural language from structured data with neural networks has become an common approach for NLG. This problem can be challenging when the form of the s…
Entity Tracking Improves Cloze-style Reading Comprehension
Luong Hoang, Sam Wiseman, Alexander M. Rush
Reading comprehension tasks test the ability of models to process long-term context and remember salient information. Recent work has shown that relatively simple neural methods su…
Bottom-Up Abstractive Summarization
Sebastian Gehrmann, Yuntian Deng, Alexander M. Rush
Neural network-based methods for abstractive summarization produce outputs that are more fluent than other techniques, but which can be poor at content selection. This work propose…
Detecting coherence via spectrum estimation
Xiao-Dong Yu, Otfried Gühne
Coherence is a basic phenomenon in quantum mechanics and considered to be an essential resource in quantum information processing. Although the quantification of coherence has attr…
Avoiding Latent Variable Collapse With Generative Skip Models
Adji B. Dieng, Yoon Kim, Alexander M. Rush +1
Variational autoencoders learn distributions of high-dimensional data. They model data with a deep latent-variable model and then fit the model by maximizing a lower bound of the l…