2 citations · 2 across the 2 of their papers we have counts for
5 papers
Data-to-text Generation with Variational Sequential Planning
Ratish Puduppully, Yao Fu, Mirella Lapata
We consider the task of data-to-text generation, which aims to create textual output from non-linguistic input. We focus on generating long-form text, i.e., documents with multiple…
Data-to-text Generation with Macro Planning
Ratish Puduppully, Mirella Lapata
Recent approaches to data-to-text generation have adopted the very successful encoder-decoder architecture or variants thereof. These models generate text which is fluent (but ofte…
Transition-Based Deep Input Linearization
Ratish Puduppully, Yue Zhang, Manish Shrivastava
Traditional methods for deep NLG adopt pipeline approaches comprising stages such as constructing syntactic input, predicting function words, linearizing the syntactic input and ge…
Data-to-text Generation with Entity Modeling
Ratish Puduppully, Li Dong, Mirella Lapata
Recent approaches to data-to-text generation have shown great promise thanks to the use of large-scale datasets and the application of neural network architectures which are traine…
Data-to-Text Generation with Content Selection and Planning
Ratish Puduppully, Li Dong, Mirella Lapata
Recent advances in data-to-text generation have led to the use of large-scale datasets and neural network models which are trained end-to-end, without explicitly modeling what to s…