Exploiting Monolingual Data at Scale for Neural Machine Translation

While target-side monolingual data has been proven to be very useful to improve neural machine translation (briefly, NMT) through back translation, source-side monolingual data is not well investigated. In this work, we study how to use both the source-side and target-side monolingual data for NMT, and propose an effective strategy leveraging both of them... (read more)

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Datasets


Results from the Paper


 Ranked #1 on Machine Translation on WMT2016 English-German (SacreBLEU metric, using extra training data)

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TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK USES EXTRA
TRAINING DATA
BENCHMARK
Machine Translation WMT2016 English-German Exploiting Mono at Scale (single) SacreBLEU 40.9 # 1
Machine Translation WMT2016 German-English Exploiting Mono at Scale (single) SacreBLEU 47.5 # 1
Machine Translation WMT2019 English-German Exploiting Mono at Scale (single) SacreBLEU 43.8 # 1
Machine Translation WMT2019 German-English Exploiting Mono at Scale (single) SacreBLEU 41.9 # 1

Methods used in the Paper


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