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arXiv · 1906.05447

Cued@wmt19:ewc&lms

Felix Stahlberg·Danielle Saunders·Adria de Gispert·Bill Byrne
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Abstract

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). We report substantial gains by fine-tuning very strong baselines on former WMT test sets using a combination of checkpoint averaging and EWC. A sentence-level Transformer LM and a document-level LM based on a modified Transformer architecture yield further gains. As in previous years, we also extract $n$-gram probabilities from SMT lattices which can be seen as a source-conditioned $n$-gram LM.

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Felix Stahlberg, Danielle Saunders, Adria de Gispert, Bill Byrne. 2019-06-11. Cued@wmt19:ewc&lms. https://arxiv.org/abs/1906.05447

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