DRAGNN: A Transition-based Framework for Dynamically Connected Neural
Networks

DRAGNN: A Transition-based Framework for Dynamically Connected Neural Networks

13 March 2017

Lingpeng Kong

Papers citing "DRAGNN: A Transition-based Framework for Dynamically Connected Neural Networks"

7 / 7 papers shown

Title
GraphPB: Graphical Representations of Prosody Boundary in Speech Synthesis Aolan Sun Jianzong Wang Ning Cheng Huayi Peng Zhen Zeng Lingwei Kong Jing Xiao 11 9 0 03 Dec 2020
Text Data Augmentation Made Simple By Leveraging NLP Cloud APIs Claude Coulombe 12 117 0 05 Dec 2018
SLING: A framework for frame semantic parsing Michael Ringgaard Rahul Gupta Fernando C Pereira 26 53 0 19 Oct 2017
A Nested Attention Neural Hybrid Model for Grammatical Error Correction Jianshu Ji Qinlong Wang Kristina Toutanova Yongen Gong Steven QH Truong Jianfeng Gao 19 110 0 07 Jul 2017
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
A Decomposable Attention Model for Natural Language Inference Ankur P. Parikh Oscar Täckström Dipanjan Das Jakob Uszkoreit 198 1,367 0 06 Jun 2016
Stack-propagation: Improved Representation Learning for Syntax Yuan Zhang David J. Weiss 29 87 0 21 Mar 2016