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.

翻译：我们提出IMTLab，这是一个开源端到端交互式机器翻译（IMT）系统平台，使研究人员能够快速构建基于最先进模型的IMT系统，执行端到端评估，并诊断系统薄弱环节。IMTLab将整个交互翻译过程视为面向任务的对话，采用人在回路模式，其中人类干预可被显式纳入以生成高质量的无误译文。为此，我们设计了通用通信接口以支持灵活的IMT架构和用户策略。基于所提出的设计，我们构建了模拟与真实交互环境以实现端到端评估，并利用该框架系统性地评估了既往IMT系统。模拟实验与人工实验表明：前缀约束解码方法在端到端评估中仍获得最低编辑成本，而BiTIIMT在实现相近编辑成本的同时提供了更佳的交互体验。