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Evaluating Syntactic Properties of Seq2seq Output with a Broad Coverage
  HPSG: A Case Study on Machine Translation

Evaluating Syntactic Properties of Seq2seq Output with a Broad Coverage HPSG: A Case Study on Machine Translation

6 September 2018
Johnny Tian-Zheng Wei
Khiem Pham
Brian Dillon
Brendan O'Connor
ArXivPDFHTML

Papers citing "Evaluating Syntactic Properties of Seq2seq Output with a Broad Coverage HPSG: A Case Study on Machine Translation"

2 / 2 papers shown
Title
RuCoLA: Russian Corpus of Linguistic Acceptability
RuCoLA: Russian Corpus of Linguistic Acceptability
Vladislav Mikhailov
T. Shamardina
Max Ryabinin
A. Pestova
I. Smurov
Ekaterina Artemova
32
28
0
23 Oct 2022
Google's Neural Machine Translation System: Bridging the Gap between
  Human and Machine Translation
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
718
6,748
0
26 Sep 2016
1