68 citations · 234 across the 28 of their papers we have counts for
5 papers · 1 filter
Evaluating Discourse Phenomena in Neural Machine Translation
Rachel Bawden, Rico Sennrich, Alexandra Birch +1
For machine translation to tackle discourse phenomena, models must have access to extra-sentential linguistic context. There has been recent interest in modelling context in neural…
The University of Edinburgh's Neural MT Systems for WMT17
Rico Sennrich, Alexandra Birch, Anna Currey +5
This paper describes the University of Edinburgh's submissions to the WMT17 shared news translation and biomedical translation tasks. We participated in 12 translation directions f…
Deep Architectures for Neural Machine Translation
Antonio Valerio Miceli Barone, Jindřich Helcl, Rico Sennrich +2
It has been shown that increasing model depth improves the quality of neural machine translation. However, different architectural variants to increase model depth have been propos…
Nematus: a Toolkit for Neural Machine Translation
Rico Sennrich, Orhan Firat, Kyunghyun Cho +8
We present Nematus, a toolkit for Neural Machine Translation. The toolkit prioritizes high translation accuracy, usability, and extensibility. Nematus has been used to build top-pe…
Predicting Target Language CCG Supertags Improves Neural Machine Translation
Maria Nadejde, Siva Reddy, Rico Sennrich +4
Neural machine translation (NMT) models are able to partially learn syntactic information from sequential lexical information. Still, some complex syntactic phenomena such as prepo…