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EAT: a simple and versatile semantic representation format for multi-purpose NLP

EAT: a simple and versatile semantic representation format for multi-purpose NLP

Tommi Gröndahl
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Papers citing "EAT: a simple and versatile semantic representation format for multi-purpose NLP"

8 / 8 papers shown
Title
Gromov-Wasserstein Alignment of Word Embedding Spaces
Gromov-Wasserstein Alignment of Word Embedding Spaces
David Alvarez-Melis
Tommi Jaakkola
OT
49
326
0
31 Aug 2018
Graph-to-Sequence Learning using Gated Graph Neural Networks
Graph-to-Sequence Learning using Gated Graph Neural Networks
Daniel Beck
Gholamreza Haffari
Trevor Cohn
GNN
62
323
0
26 Jun 2018
Stay On-Topic: Generating Context-specific Fake Restaurant Reviews
Stay On-Topic: Generating Context-specific Fake Restaurant Reviews
Mika Juuti
Bo Sun
Tatsuya Mori
Nadarajah Asokan
DeLMO
113
34
0
07 May 2018
Dear Sir or Madam, May I introduce the GYAFC Dataset: Corpus, Benchmarks and Metrics for Formality Style Transfer
Dear Sir or Madam, May I introduce the GYAFC Dataset: Corpus, Benchmarks and Metrics for Formality Style Transfer
Sudha Rao
Joel R. Tetreault
54
393
0
17 Mar 2018
Style Transfer in Text: Exploration and Evaluation
Style Transfer in Text: Exploration and Evaluation
Zhenxin Fu
Xiaoye Tan
Nanyun Peng
Dongyan Zhao
Rui Yan
46
507
0
18 Nov 2017
Word Translation Without Parallel Data
Word Translation Without Parallel Data
Alexis Conneau
Guillaume Lample
MarcÁurelio Ranzato
Ludovic Denoyer
Hervé Jégou
271
1,646
0
11 Oct 2017
Universal Semantic Parsing
Universal Semantic Parsing
Siva Reddy
Oscar Täckström
Slav Petrov
Mark Steedman
Mirella Lapata
29
105
0
10 Feb 2017
Effective Approaches to Attention-based Neural Machine Translation
Effective Approaches to Attention-based Neural Machine Translation
Thang Luong
Hieu H. Pham
Christopher D. Manning
316
7,951
0
17 Aug 2015