IMTLab: An Open-Source Platform for Building, Evaluating, and Diagnosing Interactive Machine Translation Systems
This provides a tool for researchers in machine translation to more efficiently develop and assess interactive systems, though it is incremental as it builds on existing methods.
The authors tackled the challenge of building and evaluating interactive machine translation systems by introducing IMTLab, an open-source platform that enables researchers to construct systems with state-of-the-art models and perform end-to-end evaluations. Their experiments showed that the prefix-constrained decoding approach achieved the lowest editing cost, while BiTIIMT offered comparable editing cost with a better interactive experience.
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.