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20162023
most citedSyntactically Guided Neural Machine Translation

72 citations · 102 across the 18 of their papers we have counts for

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Showing 2018 · cs.CLShow all

5 papers · 2 filters

cs.CL2018

Simple Fusion: Return of the Language Model

Felix Stahlberg, James Cross, Veselin Stoyanov

Neural Machine Translation (NMT) typically leverages monolingual data in training through backtranslation. We investigate an alternative simple method to use monolingual data for N…

cs.CL2018

An Operation Sequence Model for Explainable Neural Machine Translation

Felix Stahlberg, Danielle Saunders, Bill Byrne

We propose to achieve explainable neural machine translation (NMT) by changing the output representation to explain itself. We present a novel approach to NMT which generates the t…

cs.CL2018

The University of Cambridge's Machine Translation Systems for WMT18

Felix Stahlberg, Adria de Gispert, Bill Byrne

The University of Cambridge submission to the WMT18 news translation task focuses on the combination of diverse models of translation. We compare recurrent, convolutional, and self…

cs.CL2018

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

Danielle Saunders, Felix Stahlberg, Adria de Gispert +1

We explore strategies for incorporating target syntax into Neural Machine Translation. We specifically focus on syntax in ensembles containing multiple sentence representations. We…

cs.CL2018★ 7 cited

Why not be Versatile? Applications of the SGNMT Decoder for Machine Translation

Felix Stahlberg, Danielle Saunders, Gonzalo Iglesias +1

SGNMT is a decoding platform for machine translation which allows paring various modern neural models of translation with different kinds of constraints and symbolic models. In thi…