Modeling Coherence for Neural Machine Translation with Dynamic and Topic Caches

Understand

Sentences in a well-formed text are connected to each other via various links to form the cohesive structure of the text.

• Current neural machine translation (NMT) systems translate a text in a conventional sentence-by-sentence fashion, ignoring such cross-sentence links and dependencies.
• This may lead to generate an incoherent target text for a coherent source text.
• In order to handle this issue, we propose a cache-based approach to modeling coherence for neural machine translation by capturing contextual information either from recently translated sentences or the entire document.