  Machine translation (MT) requires a wide range of linguistic capabilities,
which current end-to-end models are expected to learn implicitly by observing
aligned sentences in bilingual corpora. In this work, we ask: \emph{How well do
MT models learn coreference resolution from implicit signal?} To answer this
question, we develop an evaluation methodology that derives coreference
clusters from MT output and evaluates them without requiring annotations in the
target language. We further evaluate several prominent open-source and
commercial MT systems, translating from English to six target languages, and
compare them to state-of-the-art coreference resolvers on three challenging
benchmarks. Our results show that the monolingual resolvers greatly outperform
MT models. Motivated by this result, we experiment with different methods for
incorporating the output of coreference resolution models in MT, showing
improvement over strong baselines.
