  Pronouns are a long-standing challenge in machine translation. We present a
study of the performance of a range of rule-based, statistical and neural MT
systems on pronoun translation based on an extensive manual evaluation using
the PROTEST test suite, which enables a fine-grained analysis of different
pronoun types and sheds light on the difficulties of the task. We find that the
rule-based approaches in our corpus perform poorly as a result of
oversimplification, whereas SMT and early NMT systems exhibit significant
shortcomings due to a lack of awareness of the functional and referential
properties of pronouns. A recent Transformer-based NMT system with
cross-sentence context shows very promising results on non-anaphoric pronouns
and intra-sentential anaphora, but there is still considerable room for
improvement in examples with cross-sentence dependencies.
