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20162020
most citedMassively Multilingual Neural Machine Translation in the Wild: Findings and Challenges

293 citations · 420 across the 4 of their papers we have counts for

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Showing cs.CLShow all

12 papers · 1 filter

cs.CL2020

Sentence Boundary Augmentation For Neural Machine Translation Robustness

Daniel Li, Te I, Naveen Arivazhagan +2

Neural Machine Translation (NMT) models have demonstrated strong state of the art performance on translation tasks where well-formed training and evaluation data are provided, but…

cs.CL202035 cited

Leveraging Monolingual Data with Self-Supervision for Multilingual Neural Machine Translation

Aditya Siddhant, Ankur Bapna, Yuan Cao +5

Over the last few years two promising research directions in low-resource neural machine translation (NMT) have emerged. The first focuses on utilizing high-resource languages to i…

cs.CL2020

Re-translation versus Streaming for Simultaneous Translation

Naveen Arivazhagan, Colin Cherry, Wolfgang Macherey +1

There has been great progress in improving streaming machine translation, a simultaneous paradigm where the system appends to a growing hypothesis as more source content becomes av…

cs.CL2019

Re-Translation Strategies For Long Form, Simultaneous, Spoken Language Translation

Naveen Arivazhagan, Colin Cherry, Te I +3

We investigate the problem of simultaneous machine translation of long-form speech content. We target a continuous speech-to-text scenario, generating translated captions for a liv…

cs.CL2019

Simple, Scalable Adaptation for Neural Machine Translation

Ankur Bapna, Naveen Arivazhagan, Orhan Firat

Fine-tuning pre-trained Neural Machine Translation (NMT) models is the dominant approach for adapting to new languages and domains. However, fine-tuning requires adapting and maint…

cs.CL2019

Investigating Multilingual NMT Representations at Scale

Sneha Reddy Kudugunta, Ankur Bapna, Isaac Caswell +2

Multilingual Neural Machine Translation (NMT) models have yielded large empirical success in transfer learning settings. However, these black-box representations are poorly underst…