Multi-representation Ensembles and Delayed SGD Updates Improve Syntax-based NMT

05/01/2018
by   Danielle Saunders, et al.
0

We explore strategies for incorporating target syntax into Neural Machine Translation. We specifically focus on syntax in ensembles containing multiple sentence representations. We formulate beam search over such ensembles using WFSTs, and describe a delayed SGD update training procedure that is especially effective for long representations like linearized syntax. Our approach gives state-of-the-art performance on a difficult Japanese-English task.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
04/16/2017

Towards String-to-Tree Neural Machine Translation

We present a simple method to incorporate syntactic information about th...
research
05/28/2018

Inducing Grammars with and for Neural Machine Translation

Machine translation systems require semantic knowledge and grammatical u...
research
11/12/2017

Syntax-Directed Attention for Neural Machine Translation

Attention mechanism, including global attention and local attention, pla...
research
05/08/2019

Syntax-Enhanced Neural Machine Translation with Syntax-Aware Word Representations

Syntax has been demonstrated highly effective in neural machine translat...
research
04/29/2020

Syntax-aware Data Augmentation for Neural Machine Translation

Data augmentation is an effective performance enhancement in neural mach...
research
08/30/2019

Latent Part-of-Speech Sequences for Neural Machine Translation

Learning target side syntactic structure has been shown to improve Neura...
research
06/15/2016

The Edit Distance Transducer in Action: The University of Cambridge English-German System at WMT16

This paper presents the University of Cambridge submission to WMT16. Mot...

Please sign up or login with your details

Forgot password? Click here to reset