  We present IMTLab, an open-source end-to-end interactive machine translation
(IMT) system platform that enables researchers to quickly build IMT systems
with state-of-the-art models, perform an end-to-end evaluation, and diagnose
the weakness of systems. IMTLab treats the whole interactive translation
process as a task-oriented dialogue with a human-in-the-loop setting, in which
human interventions can be explicitly incorporated to produce high-quality,
error-free translations. To this end, a general communication interface is
designed to support the flexible IMT architectures and user policies. Based on
the proposed design, we construct a simulated and real interactive environment
to achieve end-to-end evaluation and leverage the framework to systematically
evaluate previous IMT systems. Our simulated and manual experiments show that
the prefix-constrained decoding approach still gains the lowest editing cost in
the end-to-end evaluation, while BiTIIMT achieves comparable editing cost with
a better interactive experience.
