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

25 February 2019

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 David Alvarez-Melis Tommi Jaakkola OT 49 326 0 31 Aug 2018
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 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 Sudha Rao Joel R. Tetreault 54 393 0 17 Mar 2018
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 Alexis Conneau Guillaume Lample MarcÁurelio Ranzato Ludovic Denoyer Hervé Jégou 271 1,646 0 11 Oct 2017
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 Thang Luong Hieu H. Pham Christopher D. Manning 316 7,951 0 17 Aug 2015