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"

3 / 3 papers shown

Title
ViGGO: A Video Game Corpus for Data-To-Text Generation in Open-Domain Conversation Juraj Juraska Kevin K. Bowden M. Walker 19 41 0 26 Oct 2019
Evaluating the State-of-the-Art of End-to-End Natural Language Generation: The E2E NLG Challenge Ondrej Dusek Jekaterina Novikova Verena Rieser ELM 23 231 0 23 Jan 2019
Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation Yonghui Wu M. Schuster Z. Chen Quoc V. Le Mohammad Norouzi ... Alex Rudnick Oriol Vinyals G. Corrado Macduff Hughes J. Dean AIMat 716 6,743 0 26 Sep 2016