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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 2019 · cs.CLShow all

7 papers · 2 filters

cs.CL2019★ 2 cited

Neural Machine Translation: A Review and Survey

Felix Stahlberg

The field of machine translation (MT), the automatic translation of written text from one natural language into another, has experienced a major paradigm shift in recent years. Sta…

cs.CL2019

On NMT Search Errors and Model Errors: Cat Got Your Tongue?

Felix Stahlberg, Bill Byrne

We report on search errors and model errors in neural machine translation (NMT). We present an exact inference procedure for neural sequence models based on a combination of beam s…

cs.CL2019

The CUED's Grammatical Error Correction Systems for BEA-2019

Felix Stahlberg, Bill Byrne

We describe two entries from the Cambridge University Engineering Department to the BEA 2019 Shared Task on grammatical error correction. Our submission to the low-resource track i…

cs.CL2019

UCAM Biomedical translation at WMT19: Transfer learning multi-domain ensembles

Danielle Saunders, Felix Stahlberg, Bill Byrne

The 2019 WMT Biomedical translation task involved translating Medline abstracts. We approached this using transfer learning to obtain a series of strong neural models on distinct d…

cs.CL2019

Cued@wmt19:ewc&lms

Felix Stahlberg, Danielle Saunders, Adria de Gispert +1

Two techniques provide the fabric of the Cambridge University Engineering Department's (CUED) entry to the WMT19 evaluation campaign: elastic weight consolidation (EWC) and differe…

cs.CL2019

Domain Adaptive Inference for Neural Machine Translation

Danielle Saunders, Felix Stahlberg, Adria de Gispert +1

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

cs.CL2019

Neural Grammatical Error Correction with Finite State Transducers

Felix Stahlberg, Christopher Bryant, Bill Byrne

Grammatical error correction (GEC) is one of the areas in natural language processing in which purely neural models have not yet superseded more traditional symbolic models. Hybrid…