  The advent of context-aware NMT has resulted in promising improvements in the
overall translation quality and specifically in the translation of discourse
phenomena such as pronouns. Previous works have mainly focused on the use of
past sentences as context with a focus on anaphora translation. In this work,
we investigate the effect of future sentences as context by comparing the
performance of a contextual NMT model trained with the future context to the
one trained with the past context. Our experiments and evaluation, using
generic and pronoun-focused automatic metrics, show that the use of future
context not only achieves significant improvements over the context-agnostic
Transformer, but also demonstrates comparable and in some cases improved
performance over its counterpart trained on past context. We also perform an
evaluation on a targeted cataphora test suite and report significant gains over
the context-agnostic Transformer in terms of BLEU.
