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Better Document-level Machine Translation with Bayes’ Rule

Lei Yu ( Google Deepmind )

Thanks to the rapid development of neural sequence-to-sequence models and the availability of large scale datasets, current state-of-the-art machine translation systems have achieved super-human performance for some language pairs such as French <-> English and German <-> English. However, there’s still a big gap between humans and document-level machine translation systems, because humans are much better at capturing the consistency and coherence of the documents. In this talk, I will first review the existing approaches for document-level machine translation and then present our recent work on Better Document-level Machine Translation with Bayes’ rule. If time permits, I will also briefly introduce the research we have been doing at DeepMind related to machine translation and other language processing tasks. 

How can you join?
Closer to the date of the event you will receive an email with a link to the Microsoft Teams Meeting to join the seminar. Registration closes 2 hours before the beginning of the seminar.



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