  State-of-the-art (SOTA) neural machine translation (NMT) systems translate
texts at sentence level, ignoring context: intra-textual information, like the
previous sentence, and extra-textual information, like the gender of the
speaker. Because of that, some sentences are translated incorrectly.
Personalised NMT (PersNMT) and document-level NMT (DocNMT) incorporate this
information into the translation process. Both fields are relatively new and
previous work within them is limited. Moreover, there are no readily available
robust evaluation metrics for them, which makes it difficult to develop better
systems, as well as track global progress and compare different methods. This
thesis proposal focuses on PersNMT and DocNMT for the domain of dialogue
extracted from TV subtitles in five languages: English, Brazilian Portuguese,
German, French and Polish. Three main challenges are addressed: (1)
incorporating extra-textual information directly into NMT systems; (2)
improving the machine translation of cohesion devices; (3) reliable evaluation
for PersNMT and DocNMT.
