  Context-aware neural machine translation involves leveraging information
beyond sentence-level context to resolve inter-sentential discourse
dependencies and improve document-level translation quality, and has given rise
to a number of recent techniques. However, despite well-reasoned intuitions,
most context-aware translation models show only modest improvements over
sentence-level systems. In this work, we investigate several challenges that
impede progress within this field, relating to discourse phenomena, context
usage, model architectures, and document-level evaluation. To address these
problems, we propose a more realistic setting for document-level translation,
called paragraph-to-paragraph (para2para) translation, and collect a new
dataset of Chinese-English novels to promote future research.
