  Current approaches to machine translation (MT) either translate sentences in
isolation, disregarding the context they appear in, or model context at the
level of the full document, without a notion of any internal structure the
document may have. In this work we consider the fact that documents are rarely
homogeneous blocks of text, but rather consist of parts covering different
topics. Some documents, such as biographies and encyclopedia entries, have
highly predictable, regular structures in which sections are characterised by
different topics. We draw inspiration from Louis and Webber (2014) who use this
information to improve statistical MT and transfer their proposal into the
framework of neural MT. We compare two different methods of including
information about the topic of the section within which each sentence is found:
one using side constraints and the other using a cache-based model. We create
and release the data on which we run our experiments - parallel corpora for
three language pairs (Chinese-English, French-English, Bulgarian-English) from
Wikipedia biographies, which we extract automatically, preserving the
boundaries of sections within the articles.
