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Shape of synth to come: Why we should use synthetic data for English
  surface realization

Shape of synth to come: Why we should use synthetic data for English surface realization

6 May 2020
H. Elder
R. Burke
Alexander O’Connor
Jennifer Foster
ArXiv (abs)PDFHTML

Papers citing "Shape of synth to come: Why we should use synthetic data for English surface realization"

15 / 15 papers shown
Title
Neural data-to-text generation: A comparison between pipeline and
  end-to-end architectures
Neural data-to-text generation: A comparison between pipeline and end-to-end architectures
Thiago Castro Ferreira
Chris van der Lee
Emiel van Miltenburg
Emiel Krahmer
85
143
0
23 Aug 2019
Designing a Symbolic Intermediate Representation for Neural Surface
  Realization
Designing a Symbolic Intermediate Representation for Neural Surface Realization
H. Elder
Jennifer Foster
James Barry
Alexander O’Connor
50
13
0
24 May 2019
Step-by-Step: Separating Planning from Realization in Neural
  Data-to-Text Generation
Step-by-Step: Separating Planning from Realization in Neural Data-to-Text Generation
Amit Moryossef
Yoav Goldberg
Ido Dagan
55
184
0
06 Apr 2019
compare-mt: A Tool for Holistic Comparison of Language Generation
  Systems
compare-mt: A Tool for Holistic Comparison of Language Generation Systems
Graham Neubig
Zi-Yi Dou
Junjie Hu
Paul Michel
Danish Pruthi
Xinyi Wang
John Wieting
ELM
67
116
0
19 Mar 2019
Universal Dependency Parsing from Scratch
Universal Dependency Parsing from Scratch
Peng Qi
Timothy Dozat
Yuhao Zhang
Christopher D. Manning
78
270
0
29 Jan 2019
Generating High-Quality Surface Realizations Using Data Augmentation and
  Factored Sequence Models
Generating High-Quality Surface Realizations Using Data Augmentation and Factored Sequence Models
H. Elder
Chris Hokamp
28
19
0
20 May 2018
A Deep Ensemble Model with Slot Alignment for Sequence-to-Sequence
  Natural Language Generation
A Deep Ensemble Model with Slot Alignment for Sequence-to-Sequence Natural Language Generation
Juraj Juraska
P. Karagiannis
Kevin K. Bowden
M. Walker
80
88
0
16 May 2018
A Graph-to-Sequence Model for AMR-to-Text Generation
A Graph-to-Sequence Model for AMR-to-Text Generation
Linfeng Song
Yue Zhang
Zhiguo Wang
D. Gildea
GNNAIMat
98
254
0
07 May 2018
Neural AMR: Sequence-to-Sequence Models for Parsing and Generation
Neural AMR: Sequence-to-Sequence Models for Parsing and Generation
Ioannis Konstas
Srini Iyer
Mark Yatskar
Yejin Choi
Luke Zettlemoyer
AIMat
47
304
0
26 Apr 2017
Get To The Point: Summarization with Pointer-Generator Networks
Get To The Point: Summarization with Pointer-Generator Networks
A. See
Peter J. Liu
Christopher D. Manning
3DPC
306
4,025
0
14 Apr 2017
OpenNMT: Open-Source Toolkit for Neural Machine Translation
OpenNMT: Open-Source Toolkit for Neural Machine Translation
Guillaume Klein
Yoon Kim
Yuntian Deng
Jean Senellart
Alexander M. Rush
330
1,900
0
10 Jan 2017
Pointer Sentinel Mixture Models
Pointer Sentinel Mixture Models
Stephen Merity
Caiming Xiong
James Bradbury
R. Socher
RALM
334
2,895
0
26 Sep 2016
Teaching Machines to Read and Comprehend
Teaching Machines to Read and Comprehend
Karl Moritz Hermann
Tomás Kociský
Edward Grefenstette
L. Espeholt
W. Kay
Mustafa Suleyman
Phil Blunsom
347
3,551
0
10 Jun 2015
Pointer Networks
Pointer Networks
Oriol Vinyals
Meire Fortunato
Navdeep Jaitly
124
3,059
0
09 Jun 2015
Improved Semantic Representations From Tree-Structured Long Short-Term
  Memory Networks
Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks
Kai Sheng Tai
R. Socher
Christopher D. Manning
AIMat
142
3,122
0
28 Feb 2015
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