Rico Sennrich

TL;DR: The authors explored methods of decode-time integration of attention-based neural translation models with phrase-based statistical machine translation and achieved state-of-the-art performance for English-Russian news translation.

TL;DR: In this article, a large and diverse parallel corpus of a hundred thousands Python functions with their documentation strings ("docstrings") generated by scraping open source repositories on GitHub is introduced. And they describe baseline results for the code documentation and code generation tasks obtained by neural machine translation.

TL;DR: This work introduces syntactic information in the form of CCG supertags in the decoder, by interleaving the target supertags with the word sequence, and shows that explicitly modeling target-syntax improves machine translation quality for German->English and for Romanian->English.

TL;DR: The authors identify deixis, ellipsis and lexical cohesion as three main sources of inconsistency in context-aware NMT and introduce a model that is suitable for this scenario and demonstrate major gains over a context-agnostic baseline on new benchmarks without sacrificing performance as measured with BLEU.

TL;DR: In experiments on German to English OPUS data, and German to Romansh, a low-resource scenario, it is found that several methods improve domain robustness, reconstruction standing out as a method that not only improves automatic scores, but also shows improvements in a manual assessments of adequacy, albeit at some loss in fluency.