Cued@wmt19:ewc&lms

no code implementations • 11 Jun 2019 • Felix Stahlberg, Danielle Saunders, Adria de Gispert, Bill Byrne

Two techniques provide the fabric of the Cambridge University Engineering Department's (CUED) entry to the WMT19 evaluation campaign: elastic weight consolidation (EWC) and different forms of language modelling (LMs).

Language Modelling Sentence

Domain Adaptive Inference for Neural Machine Translation

no code implementations • ACL 2019 • Danielle Saunders, Felix Stahlberg, Adria de Gispert, Bill Byrne

We investigate adaptive ensemble weighting for Neural Machine Translation, addressing the case of improving performance on a new and potentially unknown domain without sacrificing performance on the original domain.

Machine Translation NMT +1

The University of Cambridge's Machine Translation Systems for WMT18

no code implementations • WS 2018 • 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.

Machine Translation Translation

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

no code implementations • ACL 2018 • Danielle Saunders, Felix Stahlberg, Adria de Gispert, Bill Byrne

We explore strategies for incorporating target syntax into Neural Machine Translation.

Machine Translation NMT +2