  We investigate the use of extended context in attention-based neural machine
translation. We base our experiments on translated movie subtitles and discuss
the effect of increasing the segments beyond single translation units. We study
the use of extended source language context as well as bilingual context
extensions. The models learn to distinguish between information from different
segments and are surprisingly robust with respect to translation quality. In
this pilot study, we observe interesting cross-sentential attention patterns
that improve textual coherence in translation at least in some selected cases.
