Characterizing Variation in Crowd-Sourced Data for Training Neural Language Generators to Produce Stylistically Varied Outputs

14 September 2018

Papers citing "Characterizing Variation in Crowd-Sourced Data for Training Neural Language Generators to Produce Stylistically Varied Outputs"

5 / 5 papers shown

Title
A Deep Ensemble Model with Slot Alignment for Sequence-to-Sequence Natural Language Generation Juraj Juraska P. Karagiannis Kevin K. Bowden M. Walker 75 88 0 16 May 2018
Delete, Retrieve, Generate: A Simple Approach to Sentiment and Style Transfer Juncen Li Robin Jia He He Percy Liang 86 553 0 17 Apr 2018
Dear Sir or Madam, May I introduce the GYAFC Dataset: Corpus, Benchmarks and Metrics for Formality Style Transfer Sudha Rao Joel R. Tetreault 60 397 0 17 Mar 2018
The E2E Dataset: New Challenges For End-to-End Generation Jekaterina Novikova Ondrej Dusek Verena Rieser 87 459 0 28 Jun 2017
Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation Yonghui Wu M. Schuster Zhiwen Chen Quoc V. Le Mohammad Norouzi ... Alex Rudnick Oriol Vinyals G. Corrado Macduff Hughes J. Dean AIMat 885 6,787 0 26 Sep 2016